source("functions.R")df_analysis <- readr::read_csv("output/df_analysis.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## prefec_kanji = col_character(),
## prefecture = col_character(),
## date = col_date(format = ""),
## prefec = col_character(),
## prefec_kanji2 = col_character()
## )
## See spec(...) for full column specifications.
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ===================================
## Model 1
## -----------------------------------
## treat_var:date_2018_01 73.091
## (60.379)
## treat_var:date_2018_02 76.622
## (59.194)
## treat_var:date_2018_03 79.450
## (58.706)
## treat_var:date_2018_04 80.316
## (50.612)
## treat_var:date_2018_05 80.918
## (47.239)
## treat_var:date_2018_06 80.880
## (42.132)
## treat_var:date_2018_07 86.139 *
## (41.202)
## treat_var:date_2018_08 94.030 *
## (39.395)
## treat_var:date_2018_09 95.236 *
## (36.800)
## treat_var:date_2018_10 101.426 **
## (36.271)
## treat_var:date_2018_11 96.104 **
## (34.564)
## treat_var:date_2018_12 87.144 **
## (32.350)
## treat_var:date_2019_01 38.738
## (31.557)
## treat_var:date_2019_02 36.324
## (27.708)
## treat_var:date_2019_03 34.589
## (26.308)
## treat_var:date_2019_04 30.890
## (21.851)
## treat_var:date_2019_05 38.728 *
## (18.633)
## treat_var:date_2019_06 40.432 *
## (16.390)
## treat_var:date_2019_07 40.439 *
## (15.344)
## treat_var:date_2019_08 44.271 **
## (16.240)
## treat_var:date_2019_09 41.848 **
## (13.694)
## treat_var:date_2019_10 39.937 *
## (15.821)
## treat_var:date_2019_11 37.692 *
## (15.163)
## treat_var:date_2019_12 42.847 **
## (14.903)
## treat_var:date_2020_02 10.302
## (6.095)
## treat_var:date_2020_03 13.097
## (9.675)
## treat_var:date_2020_04 27.146
## (14.378)
## treat_var:date_2020_05 44.688 *
## (20.246)
## treat_var:date_2020_06 61.357 **
## (22.494)
## treat_var:date_2020_07 67.767 *
## (27.241)
## treat_var:date_2020_08 72.448 *
## (29.814)
## treat_var:date_2020_09 76.712 *
## (33.186)
## -----------------------------------
## R^2 1.000
## Adj. R^2 0.999
## Num. obs. 1551
## RMSE 15.156
## N Clusters 47
## ===================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_OLS_notrend")
# Event study graph
graph_hogo_persons_OLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_OLS_notrend")
graph_hogo_persons_OLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_OLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 164.647
## (93.609)
## treat_var:date_2018_02 164.380
## (90.440)
## treat_var:date_2018_03 165.348
## (88.988)
## treat_var:date_2018_04 159.045
## (79.611)
## treat_var:date_2018_05 158.413 *
## (74.898)
## treat_var:date_2018_06 150.634 *
## (67.865)
## treat_var:date_2018_07 157.864 *
## (66.933)
## treat_var:date_2018_08 162.869 *
## (63.880)
## treat_var:date_2018_09 157.010 *
## (59.204)
## treat_var:date_2018_10 164.726 **
## (58.781)
## treat_var:date_2018_11 155.533 **
## (56.196)
## treat_var:date_2018_12 143.178 **
## (53.265)
## treat_var:date_2019_01 85.476
## (49.307)
## treat_var:date_2019_02 77.981
## (44.049)
## treat_var:date_2019_03 73.308
## (42.297)
## treat_var:date_2019_04 63.036
## (35.520)
## treat_var:date_2019_05 65.576 *
## (29.347)
## treat_var:date_2019_06 61.943 *
## (23.858)
## treat_var:date_2019_07 62.373 **
## (21.687)
## treat_var:date_2019_08 60.473 **
## (18.641)
## treat_var:date_2019_09 57.016 ***
## (15.772)
## treat_var:date_2019_10 55.323 **
## (16.246)
## treat_var:date_2019_11 49.842 ***
## (14.102)
## treat_var:date_2019_12 52.524 ***
## (13.358)
## treat_var:date_2020_02 8.799
## (5.363)
## treat_var:date_2020_03 11.523
## (7.337)
## treat_var:date_2020_04 22.950
## (12.721)
## treat_var:date_2020_05 38.235
## (19.207)
## treat_var:date_2020_06 57.527 *
## (23.389)
## treat_var:date_2020_07 61.979 *
## (27.633)
## treat_var:date_2020_08 64.900 *
## (31.958)
## treat_var:date_2020_09 65.543
## (35.842)
## ------------------------------------
## R^2 1.000
## Adj. R^2 0.999
## Num. obs. 1551
## RMSE 867.772
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_WLS_notrend")
# Event study graph
graph_hogo_persons_WLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_WLS_notrend")
graph_hogo_persons_WLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_WLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 6.576
## (3.592)
## treat_var:date_2018_03 12.450
## (6.385)
## treat_var:date_2018_04 16.362
## (9.818)
## treat_var:date_2018_05 20.008 *
## (9.017)
## treat_var:date_2018_06 23.016
## (13.053)
## treat_var:date_2018_07 31.320 *
## (13.396)
## treat_var:date_2018_08 42.257 **
## (13.474)
## treat_var:date_2018_09 46.509 **
## (14.117)
## treat_var:date_2018_10 55.744 ***
## (12.375)
## treat_var:date_2018_11 53.468 ***
## (13.902)
## treat_var:date_2018_12 47.553 **
## (14.333)
## treat_var:date_2019_01 2.193
## (7.373)
## treat_var:date_2019_02 2.824
## (7.047)
## treat_var:date_2019_03 4.134
## (8.690)
## treat_var:date_2019_04 3.481
## (10.744)
## treat_var:date_2019_05 14.365
## (14.163)
## treat_var:date_2019_06 19.114
## (15.824)
## treat_var:date_2019_07 22.167
## (15.039)
## treat_var:date_2019_08 29.044
## (18.111)
## treat_var:date_2019_09 29.666
## (14.791)
## treat_var:date_2019_10 30.800
## (16.942)
## treat_var:date_2019_11 31.602
## (16.457)
## treat_var:date_2019_12 39.802 *
## (15.635)
## treat_var:date_2020_02 13.347 *
## (5.346)
## treat_var:date_2020_03 19.188 *
## (8.682)
## treat_var:date_2020_04 36.282 **
## (10.774)
## treat_var:date_2020_05 56.870 ***
## (15.203)
## treat_var:date_2020_06 76.585 ***
## (16.995)
## treat_var:date_2020_07 86.040 ***
## (19.896)
## treat_var:date_2020_08 93.766 ***
## (21.521)
## treat_var:date_2020_09 101.076 ***
## (23.041)
## as.factor(id)1:year_month_id -3.911 ***
## (0.074)
## as.factor(id)2:year_month_id -1.258 ***
## (0.023)
## as.factor(id)3:year_month_id -0.963 ***
## (0.040)
## as.factor(id)4:year_month_id 0.285 ***
## (0.026)
## as.factor(id)5:year_month_id -2.094 ***
## (0.040)
## as.factor(id)6:year_month_id -0.010
## (0.042)
## as.factor(id)7:year_month_id
##
## as.factor(id)8:year_month_id -0.420 ***
## (0.076)
## as.factor(id)9:year_month_id -2.186 ***
## (0.077)
## as.factor(id)10:year_month_id -1.147 ***
## (0.073)
## as.factor(id)11:year_month_id -1.529 ***
## (0.058)
## as.factor(id)12:year_month_id -0.542 ***
## (0.095)
## as.factor(id)13:year_month_id -4.255 ***
## (0.080)
## as.factor(id)14:year_month_id -2.557 ***
## (0.071)
## as.factor(id)15:year_month_id -0.988 ***
## (0.057)
## as.factor(id)16:year_month_id -0.399 ***
## (0.086)
## as.factor(id)17:year_month_id -1.932 ***
## (0.050)
## as.factor(id)18:year_month_id -0.459 ***
## (0.042)
## as.factor(id)19:year_month_id -0.826 ***
## (0.058)
## as.factor(id)20:year_month_id -1.005 ***
## (0.044)
## as.factor(id)21:year_month_id -1.504 ***
## (0.118)
## as.factor(id)22:year_month_id -0.583 ***
## (0.101)
## as.factor(id)23:year_month_id -2.266 ***
## (0.112)
## as.factor(id)24:year_month_id -1.968 ***
## (0.081)
## as.factor(id)25:year_month_id -2.175 ***
## (0.089)
## as.factor(id)26:year_month_id -4.846 ***
## (0.102)
## as.factor(id)27:year_month_id -5.911 ***
## (0.091)
## as.factor(id)28:year_month_id -3.428 ***
## (0.084)
## as.factor(id)29:year_month_id -4.232 ***
## (0.077)
## as.factor(id)30:year_month_id -2.524 ***
## (0.100)
## as.factor(id)31:year_month_id -3.213 ***
## (0.041)
## as.factor(id)32:year_month_id -2.196 ***
## (0.079)
## as.factor(id)33:year_month_id -2.898 ***
## (0.030)
## as.factor(id)34:year_month_id -3.775 ***
## (0.088)
## as.factor(id)35:year_month_id -3.390 ***
## (0.042)
## as.factor(id)36:year_month_id -3.340 ***
## (0.049)
## as.factor(id)37:year_month_id -1.861 ***
## (0.040)
## as.factor(id)38:year_month_id -2.321 ***
## (0.046)
## as.factor(id)39:year_month_id -3.826 ***
## (0.004)
## as.factor(id)40:year_month_id -4.534 ***
## (0.073)
## as.factor(id)41:year_month_id -0.854 ***
## (0.025)
## as.factor(id)42:year_month_id -3.204 ***
## (0.023)
## as.factor(id)43:year_month_id -1.762 ***
## (0.049)
## as.factor(id)44:year_month_id -1.208 ***
## (0.072)
## as.factor(id)45:year_month_id -1.153 ***
## (0.092)
## as.factor(id)46:year_month_id -1.896 ***
## (0.001)
## as.factor(id)47:year_month_id 1.015 ***
## (0.079)
## -------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 4.710
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_OLS_trend")
# Event study graph
graph_hogo_persons_OLS_trend <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_OLS_trend")
graph_hogo_persons_OLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_OLS_trend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 6.589 *
## (3.258)
## treat_var:date_2018_03 14.413 *
## (6.157)
## treat_var:date_2018_04 14.966 *
## (7.308)
## treat_var:date_2018_05 21.190 *
## (8.911)
## treat_var:date_2018_06 20.266
## (12.050)
## treat_var:date_2018_07 34.352 **
## (11.340)
## treat_var:date_2018_08 46.214 ***
## (12.310)
## treat_var:date_2018_09 47.210 **
## (13.616)
## treat_var:date_2018_10 61.782 ***
## (12.944)
## treat_var:date_2018_11 59.445 ***
## (15.109)
## treat_var:date_2018_12 53.946 ***
## (14.946)
## treat_var:date_2019_01 3.074
## (7.449)
## treat_var:date_2019_02 2.442
## (7.468)
## treat_var:date_2019_03 4.631
## (9.006)
## treat_var:date_2019_04 1.222
## (9.509)
## treat_var:date_2019_05 10.623
## (11.654)
## treat_var:date_2019_06 13.853
## (13.389)
## treat_var:date_2019_07 21.145
## (12.722)
## treat_var:date_2019_08 26.108
## (14.374)
## treat_var:date_2019_09 29.513 *
## (13.394)
## treat_var:date_2019_10 34.682 *
## (13.936)
## treat_var:date_2019_11 36.064 *
## (13.507)
## treat_var:date_2019_12 45.609 **
## (13.088)
## treat_var:date_2020_02 15.668 ***
## (4.331)
## treat_var:date_2020_03 25.261 **
## (8.239)
## treat_var:date_2020_04 43.557 ***
## (8.379)
## treat_var:date_2020_05 65.711 ***
## (10.806)
## treat_var:date_2020_06 91.872 ***
## (13.392)
## treat_var:date_2020_07 103.193 ***
## (16.483)
## treat_var:date_2020_08 112.983 ***
## (17.829)
## treat_var:date_2020_09 120.495 ***
## (18.358)
## as.factor(id)1:year_month_id -1.712 ***
## (0.004)
## as.factor(id)2:year_month_id 0.985 ***
## (0.048)
## as.factor(id)3:year_month_id 1.339 ***
## (0.101)
## as.factor(id)4:year_month_id 2.528 ***
## (0.045)
## as.factor(id)5:year_month_id 0.141 ***
## (0.033)
## as.factor(id)6:year_month_id 2.218 ***
## (0.031)
## as.factor(id)7:year_month_id 2.268 ***
## (0.067)
## as.factor(id)8:year_month_id 1.780 ***
## (0.002)
## as.factor(id)9:year_month_id 0.010 ***
## (0.002)
## as.factor(id)10:year_month_id 1.054 ***
## (0.004)
## as.factor(id)11:year_month_id 0.685 ***
## (0.017)
## as.factor(id)12:year_month_id 1.639 ***
## (0.014)
## as.factor(id)13:year_month_id -2.060 ***
## (0.001)
## as.factor(id)14:year_month_id -0.354 ***
## (0.007)
## as.factor(id)15:year_month_id 1.228 ***
## (0.018)
## as.factor(id)16:year_month_id 1.790 ***
## (0.006)
## as.factor(id)17:year_month_id 0.289 ***
## (0.025)
## as.factor(id)18:year_month_id 1.769 ***
## (0.031)
## as.factor(id)19:year_month_id 1.389 ***
## (0.018)
## as.factor(id)20:year_month_id 1.223 ***
## (0.030)
## as.factor(id)21:year_month_id 0.656 ***
## (0.034)
## as.factor(id)22:year_month_id 1.593 ***
## (0.019)
## as.factor(id)23:year_month_id -0.101 **
## (0.029)
## as.factor(id)24:year_month_id 0.224 ***
## (0.002)
## as.factor(id)25:year_month_id 0.011
## (0.009)
## as.factor(id)26:year_month_id -2.672 ***
## (0.020)
## as.factor(id)27:year_month_id -3.727 ***
## (0.010)
## as.factor(id)28:year_month_id -1.237 ***
## (0.005)
## as.factor(id)29:year_month_id -2.033 ***
## (0.001)
## as.factor(id)30:year_month_id -0.345 ***
## (0.018)
## as.factor(id)31:year_month_id -0.982 ***
## (0.032)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -0.659 ***
## (0.042)
## as.factor(id)34:year_month_id -1.589 ***
## (0.008)
## as.factor(id)35:year_month_id -1.161 ***
## (0.031)
## as.factor(id)36:year_month_id -1.121 ***
## (0.025)
## as.factor(id)37:year_month_id 0.369 ***
## (0.033)
## as.factor(id)38:year_month_id -0.096 **
## (0.028)
## as.factor(id)39:year_month_id -1.556 ***
## (0.071)
## as.factor(id)40:year_month_id -2.333 ***
## (0.005)
## as.factor(id)41:year_month_id 1.435 ***
## (0.089)
## as.factor(id)42:year_month_id -0.916 ***
## (0.087)
## as.factor(id)43:year_month_id 0.460 ***
## (0.026)
## as.factor(id)44:year_month_id 0.996 ***
## (0.006)
## as.factor(id)45:year_month_id 1.033 ***
## (0.011)
## as.factor(id)46:year_month_id 0.371 ***
## (0.068)
## as.factor(id)47:year_month_id 3.210 ***
## (0.000)
## -------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 242.241
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_WLS_trend")
# Event study graph
graph_hogo_persons_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_WLS_trend")
graph_hogo_persons_WLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_WLS_trend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ===================================================================
## Model 1
## -------------------------------------------------------------------
## treat_var:date_2018_01 73.091
## (61.767)
## treat_var:date_2018_02 76.622
## (60.555)
## treat_var:date_2018_03 79.450
## (60.056)
## treat_var:date_2018_04 80.316
## (51.776)
## treat_var:date_2018_05 80.918
## (48.325)
## treat_var:date_2018_06 80.880
## (43.101)
## treat_var:date_2018_07 86.139 *
## (42.149)
## treat_var:date_2018_08 94.030 *
## (40.301)
## treat_var:date_2018_09 95.236 *
## (37.646)
## treat_var:date_2018_10 101.426 **
## (37.105)
## treat_var:date_2018_11 96.104 **
## (35.358)
## treat_var:date_2018_12 87.144 *
## (33.094)
## treat_var:date_2019_01 38.738
## (32.282)
## treat_var:date_2019_02 36.324
## (28.345)
## treat_var:date_2019_03 34.589
## (26.912)
## treat_var:date_2019_04 30.890
## (22.353)
## treat_var:date_2019_05 38.728 *
## (19.061)
## treat_var:date_2019_06 40.432 *
## (16.767)
## treat_var:date_2019_07 40.439 *
## (15.697)
## treat_var:date_2019_08 44.271 *
## (16.614)
## treat_var:date_2019_09 41.848 **
## (14.009)
## treat_var:date_2019_10 39.937 *
## (16.184)
## treat_var:date_2019_11 37.692 *
## (15.512)
## treat_var:date_2019_12 42.847 **
## (15.245)
## treat_var:date_2020_02 40.732
## (22.716)
## treat_var:date_2020_03 46.386
## (23.169)
## treat_var:date_2020_04 48.376 *
## (23.810)
## treat_var:date_2020_05 48.049
## (24.960)
## treat_var:date_2020_06 45.275
## (26.470)
## treat_var:date_2020_07 45.020
## (28.854)
## treat_var:date_2020_08 37.399
## (29.895)
## treat_var:date_2020_09 37.578
## (31.363)
## date_2020_02:google_mobility_index_2020may 1.144
## (1.220)
## date_2020_03:google_mobility_index_2020may 1.356
## (1.250)
## date_2020_04:google_mobility_index_2020may 1.266
## (1.394)
## date_2020_05:google_mobility_index_2020may 0.973
## (1.592)
## date_2020_06:google_mobility_index_2020may 0.417
## (1.683)
## date_2020_07:google_mobility_index_2020may 0.278
## (1.882)
## date_2020_08:google_mobility_index_2020may -0.299
## (1.935)
## date_2020_09:google_mobility_index_2020may -0.310
## (2.017)
## date_2020_02:infection_rate_cumulative2020jun 0.367
## (1.040)
## date_2020_03:infection_rate_cumulative2020jun 0.193
## (1.109)
## date_2020_04:infection_rate_cumulative2020jun 0.308
## (1.205)
## date_2020_05:infection_rate_cumulative2020jun -0.133
## (1.347)
## date_2020_06:infection_rate_cumulative2020jun -0.340
## (1.387)
## date_2020_07:infection_rate_cumulative2020jun -0.467
## (1.489)
## date_2020_08:infection_rate_cumulative2020jun -0.751
## (1.524)
## date_2020_09:infection_rate_cumulative2020jun -0.688
## (1.612)
## date_2020_02:death_rate_cumulative2020jun 0.086
## (10.875)
## date_2020_03:death_rate_cumulative2020jun 1.993
## (11.529)
## date_2020_04:death_rate_cumulative2020jun 0.644
## (12.713)
## date_2020_05:death_rate_cumulative2020jun 4.114
## (14.193)
## date_2020_06:death_rate_cumulative2020jun 5.468
## (14.837)
## date_2020_07:death_rate_cumulative2020jun 6.783
## (15.682)
## date_2020_08:death_rate_cumulative2020jun 8.457
## (16.267)
## date_2020_09:death_rate_cumulative2020jun 8.241
## (16.907)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.007
## (0.004)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.008 *
## (0.004)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.008
## (0.004)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.007
## (0.005)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.007
## (0.006)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.007
## (0.006)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.007
## (0.006)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.008
## (0.007)
## date_2020_02:Secondary_industry_ratio -141.933
## (107.334)
## date_2020_03:Secondary_industry_ratio -187.082
## (109.395)
## date_2020_04:Secondary_industry_ratio -139.003
## (123.111)
## date_2020_05:Secondary_industry_ratio -99.953
## (139.048)
## date_2020_06:Secondary_industry_ratio -45.746
## (146.191)
## date_2020_07:Secondary_industry_ratio -35.318
## (157.903)
## date_2020_08:Secondary_industry_ratio -13.901
## (165.697)
## date_2020_09:Secondary_industry_ratio -4.129
## (169.729)
## date_2020_02:Tertiary_industry_ratio -260.091
## (132.364)
## date_2020_03:Tertiary_industry_ratio -296.166 *
## (132.083)
## date_2020_04:Tertiary_industry_ratio -304.139
## (152.197)
## date_2020_05:Tertiary_industry_ratio -276.417
## (171.315)
## date_2020_06:Tertiary_industry_ratio -252.720
## (184.775)
## date_2020_07:Tertiary_industry_ratio -275.925
## (196.109)
## date_2020_08:Tertiary_industry_ratio -268.753
## (202.604)
## date_2020_09:Tertiary_industry_ratio -280.329
## (209.991)
## date_2020_02:Total_population 0.001
## (0.023)
## date_2020_03:Total_population 0.004
## (0.023)
## date_2020_04:Total_population 0.002
## (0.026)
## date_2020_05:Total_population -0.005
## (0.031)
## date_2020_06:Total_population -0.003
## (0.033)
## date_2020_07:Total_population -0.005
## (0.035)
## date_2020_08:Total_population -0.005
## (0.037)
## date_2020_09:Total_population -0.003
## (0.038)
## date_2020_02:Ratio_of_aged_population -1.424 *
## (0.680)
## date_2020_03:Ratio_of_aged_population -1.683 *
## (0.720)
## date_2020_04:Ratio_of_aged_population -1.834 *
## (0.781)
## date_2020_05:Ratio_of_aged_population -2.181 *
## (0.897)
## date_2020_06:Ratio_of_aged_population -2.097 *
## (0.920)
## date_2020_07:Ratio_of_aged_population -2.203 *
## (1.027)
## date_2020_08:Ratio_of_aged_population -2.240 *
## (1.067)
## date_2020_09:Ratio_of_aged_population -2.324 *
## (1.093)
## -------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 13.756
## N Clusters 47
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_OLS_notrend")
# Event study graph
graph_hogo_persons_OLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_OLS_notrend")
graph_hogo_persons_OLS_notrend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_OLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01 164.666
## (95.813)
## treat_var:date_2018_02 164.399
## (92.572)
## treat_var:date_2018_03 165.366
## (91.087)
## treat_var:date_2018_04 159.064
## (81.492)
## treat_var:date_2018_05 158.432 *
## (76.671)
## treat_var:date_2018_06 150.652 *
## (69.479)
## treat_var:date_2018_07 157.883 *
## (68.528)
## treat_var:date_2018_08 162.888 *
## (65.405)
## treat_var:date_2018_09 157.028 *
## (60.621)
## treat_var:date_2018_10 164.745 **
## (60.191)
## treat_var:date_2018_11 155.552 **
## (57.551)
## treat_var:date_2018_12 143.197 *
## (54.553)
## treat_var:date_2019_01 85.485
## (50.471)
## treat_var:date_2019_02 77.990
## (45.093)
## treat_var:date_2019_03 73.317
## (43.302)
## treat_var:date_2019_04 63.045
## (36.368)
## treat_var:date_2019_05 65.585 *
## (30.053)
## treat_var:date_2019_06 61.952 *
## (24.437)
## treat_var:date_2019_07 62.381 **
## (22.215)
## treat_var:date_2019_08 60.482 **
## (19.097)
## treat_var:date_2019_09 57.025 ***
## (16.159)
## treat_var:date_2019_10 55.332 **
## (16.646)
## treat_var:date_2019_11 49.851 **
## (14.444)
## treat_var:date_2019_12 52.533 ***
## (13.682)
## treat_var:date_2020_02 25.024
## (38.292)
## treat_var:date_2020_03 33.857
## (39.014)
## treat_var:date_2020_04 24.327
## (41.303)
## treat_var:date_2020_05 6.187
## (44.735)
## treat_var:date_2020_06 -3.992
## (46.584)
## treat_var:date_2020_07 -9.503
## (49.391)
## treat_var:date_2020_08 -23.597
## (52.119)
## treat_var:date_2020_09 -28.258
## (54.628)
## date_2020_02:google_mobility_index_2020may -1.845
## (1.953)
## date_2020_03:google_mobility_index_2020may -1.794
## (1.957)
## date_2020_04:google_mobility_index_2020may -2.098
## (2.255)
## date_2020_05:google_mobility_index_2020may -3.099
## (2.609)
## date_2020_06:google_mobility_index_2020may -3.861
## (2.744)
## date_2020_07:google_mobility_index_2020may -4.126
## (2.874)
## date_2020_08:google_mobility_index_2020may -4.839
## (3.019)
## date_2020_09:google_mobility_index_2020may -5.179
## (3.175)
## date_2020_02:infection_rate_cumulative2020jun -1.170
## (1.206)
## date_2020_03:infection_rate_cumulative2020jun -1.508
## (1.260)
## date_2020_04:infection_rate_cumulative2020jun -1.364
## (1.392)
## date_2020_05:infection_rate_cumulative2020jun -1.768
## (1.531)
## date_2020_06:infection_rate_cumulative2020jun -1.978
## (1.582)
## date_2020_07:infection_rate_cumulative2020jun -2.214
## (1.641)
## date_2020_08:infection_rate_cumulative2020jun -2.391
## (1.673)
## date_2020_09:infection_rate_cumulative2020jun -2.605
## (1.806)
## date_2020_02:death_rate_cumulative2020jun 10.144
## (13.367)
## date_2020_03:death_rate_cumulative2020jun 13.902
## (13.637)
## date_2020_04:death_rate_cumulative2020jun 11.406
## (15.587)
## date_2020_05:death_rate_cumulative2020jun 13.030
## (17.525)
## date_2020_06:death_rate_cumulative2020jun 13.209
## (18.552)
## date_2020_07:death_rate_cumulative2020jun 15.340
## (19.031)
## date_2020_08:death_rate_cumulative2020jun 15.098
## (19.888)
## date_2020_09:death_rate_cumulative2020jun 17.189
## (21.124)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.007 *
## (0.003)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.007 *
## (0.003)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.008 *
## (0.004)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.008
## (0.005)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.008
## (0.005)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.008
## (0.005)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.009
## (0.006)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.009
## (0.006)
## date_2020_02:Secondary_industry_ratio -140.014
## (123.224)
## date_2020_03:Secondary_industry_ratio -185.614
## (123.016)
## date_2020_04:Secondary_industry_ratio -136.519
## (143.877)
## date_2020_05:Secondary_industry_ratio -100.590
## (163.081)
## date_2020_06:Secondary_industry_ratio -41.896
## (173.054)
## date_2020_07:Secondary_industry_ratio -35.952
## (182.741)
## date_2020_08:Secondary_industry_ratio -10.684
## (193.993)
## date_2020_09:Secondary_industry_ratio -3.860
## (200.996)
## date_2020_02:Tertiary_industry_ratio -243.474
## (151.387)
## date_2020_03:Tertiary_industry_ratio -268.618
## (151.136)
## date_2020_04:Tertiary_industry_ratio -281.393
## (175.265)
## date_2020_05:Tertiary_industry_ratio -264.949
## (194.521)
## date_2020_06:Tertiary_industry_ratio -236.443
## (204.439)
## date_2020_07:Tertiary_industry_ratio -264.631
## (213.324)
## date_2020_08:Tertiary_industry_ratio -259.393
## (221.978)
## date_2020_09:Tertiary_industry_ratio -263.379
## (235.121)
## date_2020_02:Total_population 0.004
## (0.020)
## date_2020_03:Total_population 0.006
## (0.020)
## date_2020_04:Total_population 0.007
## (0.024)
## date_2020_05:Total_population 0.002
## (0.028)
## date_2020_06:Total_population 0.006
## (0.029)
## date_2020_07:Total_population 0.006
## (0.031)
## date_2020_08:Total_population 0.007
## (0.033)
## date_2020_09:Total_population 0.009
## (0.035)
## date_2020_02:Ratio_of_aged_population -0.760
## (0.778)
## date_2020_03:Ratio_of_aged_population -0.843
## (0.813)
## date_2020_04:Ratio_of_aged_population -1.001
## (0.909)
## date_2020_05:Ratio_of_aged_population -1.280
## (1.009)
## date_2020_06:Ratio_of_aged_population -1.221
## (1.039)
## date_2020_07:Ratio_of_aged_population -1.311
## (1.110)
## date_2020_08:Ratio_of_aged_population -1.365
## (1.169)
## date_2020_09:Ratio_of_aged_population -1.356
## (1.208)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 757.288
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_WLS_notrend")
# Event study graph
graph_hogo_persons_WLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_WLS_notrend")
graph_hogo_persons_WLS_notrend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_WLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 6.576
## (3.677)
## treat_var:date_2018_03 12.450
## (6.537)
## treat_var:date_2018_04 16.362
## (10.051)
## treat_var:date_2018_05 20.008 *
## (9.232)
## treat_var:date_2018_06 23.016
## (13.363)
## treat_var:date_2018_07 31.320 *
## (13.714)
## treat_var:date_2018_08 42.257 **
## (13.794)
## treat_var:date_2018_09 46.509 **
## (14.452)
## treat_var:date_2018_10 55.744 ***
## (12.669)
## treat_var:date_2018_11 53.468 ***
## (14.233)
## treat_var:date_2018_12 47.553 **
## (14.674)
## treat_var:date_2019_01 2.193
## (7.548)
## treat_var:date_2019_02 2.824
## (7.214)
## treat_var:date_2019_03 4.134
## (8.897)
## treat_var:date_2019_04 3.481
## (10.999)
## treat_var:date_2019_05 14.365
## (14.499)
## treat_var:date_2019_06 19.114
## (16.199)
## treat_var:date_2019_07 22.167
## (15.397)
## treat_var:date_2019_08 29.044
## (18.541)
## treat_var:date_2019_09 29.666
## (15.142)
## treat_var:date_2019_10 30.800
## (17.344)
## treat_var:date_2019_11 31.602
## (16.848)
## treat_var:date_2019_12 39.802 *
## (16.007)
## treat_var:date_2020_02 38.118 *
## (17.593)
## treat_var:date_2020_03 46.383 *
## (19.262)
## treat_var:date_2020_04 50.982 *
## (20.344)
## treat_var:date_2020_05 53.266 *
## (20.472)
## treat_var:date_2020_06 53.102 *
## (20.421)
## treat_var:date_2020_07 55.456 *
## (22.396)
## treat_var:date_2020_08 50.446 *
## (23.061)
## treat_var:date_2020_09 53.235
## (26.831)
## date_2020_02:google_mobility_index_2020may 0.266
## (0.715)
## date_2020_03:google_mobility_index_2020may 0.411
## (0.727)
## date_2020_04:google_mobility_index_2020may 0.254
## (0.713)
## date_2020_05:google_mobility_index_2020may -0.107
## (0.649)
## date_2020_06:google_mobility_index_2020may -0.731
## (0.736)
## date_2020_07:google_mobility_index_2020may -0.937
## (0.874)
## date_2020_08:google_mobility_index_2020may -1.582
## (0.862)
## date_2020_09:google_mobility_index_2020may -1.661
## (0.994)
## date_2020_02:infection_rate_cumulative2020jun 0.010
## (0.384)
## date_2020_03:infection_rate_cumulative2020jun -0.190
## (0.427)
## date_2020_04:infection_rate_cumulative2020jun -0.103
## (0.415)
## date_2020_05:infection_rate_cumulative2020jun -0.572
## (0.466)
## date_2020_06:infection_rate_cumulative2020jun -0.806
## (0.557)
## date_2020_07:infection_rate_cumulative2020jun -0.960
## (0.687)
## date_2020_08:infection_rate_cumulative2020jun -1.272
## (0.724)
## date_2020_09:infection_rate_cumulative2020jun -1.237
## (0.776)
## date_2020_02:death_rate_cumulative2020jun 2.277
## (3.795)
## date_2020_03:death_rate_cumulative2020jun 4.353
## (4.300)
## date_2020_04:death_rate_cumulative2020jun 3.173
## (4.169)
## date_2020_05:death_rate_cumulative2020jun 6.812
## (4.589)
## date_2020_06:death_rate_cumulative2020jun 8.334
## (5.459)
## date_2020_07:death_rate_cumulative2020jun 9.817
## (6.543)
## date_2020_08:death_rate_cumulative2020jun 11.660
## (7.070)
## date_2020_09:death_rate_cumulative2020jun 11.612
## (7.576)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.002
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.002
## (0.002)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.002)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.002)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.002)
## date_2020_02:Secondary_industry_ratio -78.039
## (47.326)
## date_2020_03:Secondary_industry_ratio -118.273 *
## (51.567)
## date_2020_04:Secondary_industry_ratio -65.280
## (50.607)
## date_2020_05:Secondary_industry_ratio -21.315
## (46.110)
## date_2020_06:Secondary_industry_ratio 37.807
## (55.480)
## date_2020_07:Secondary_industry_ratio 53.150
## (59.652)
## date_2020_08:Secondary_industry_ratio 79.482
## (59.198)
## date_2020_09:Secondary_industry_ratio 94.169
## (68.963)
## date_2020_02:Tertiary_industry_ratio -62.843
## (49.105)
## date_2020_03:Tertiary_industry_ratio -83.745
## (58.722)
## date_2020_04:Tertiary_industry_ratio -76.545
## (57.489)
## date_2020_05:Tertiary_industry_ratio -33.650
## (59.261)
## date_2020_06:Tertiary_industry_ratio 5.220
## (69.277)
## date_2020_07:Tertiary_industry_ratio -2.811
## (73.358)
## date_2020_08:Tertiary_industry_ratio 19.534
## (75.496)
## date_2020_09:Tertiary_industry_ratio 23.130
## (80.030)
## date_2020_02:Total_population 0.009
## (0.008)
## date_2020_03:Total_population 0.013
## (0.011)
## date_2020_04:Total_population 0.012
## (0.009)
## date_2020_05:Total_population 0.005
## (0.007)
## date_2020_06:Total_population 0.008
## (0.008)
## date_2020_07:Total_population 0.006
## (0.009)
## date_2020_08:Total_population 0.007
## (0.009)
## date_2020_09:Total_population 0.010
## (0.010)
## date_2020_02:Ratio_of_aged_population 0.325
## (0.326)
## date_2020_03:Ratio_of_aged_population 0.200
## (0.317)
## date_2020_04:Ratio_of_aged_population 0.184
## (0.320)
## date_2020_05:Ratio_of_aged_population -0.029
## (0.285)
## date_2020_06:Ratio_of_aged_population 0.189
## (0.349)
## date_2020_07:Ratio_of_aged_population 0.218
## (0.395)
## date_2020_08:Ratio_of_aged_population 0.316
## (0.400)
## date_2020_09:Ratio_of_aged_population 0.366
## (0.453)
## as.factor(id)1:year_month_id
##
## as.factor(id)2:year_month_id 2.791 ***
## (0.180)
## as.factor(id)3:year_month_id 2.931 ***
## (0.182)
## as.factor(id)4:year_month_id 4.273 ***
## (0.193)
## as.factor(id)5:year_month_id 1.914 ***
## (0.183)
## as.factor(id)6:year_month_id 4.042 ***
## (0.164)
## as.factor(id)7:year_month_id 3.921 ***
## (0.177)
## as.factor(id)8:year_month_id 3.485 ***
## (0.155)
## as.factor(id)9:year_month_id 1.784 ***
## (0.182)
## as.factor(id)10:year_month_id 2.638 ***
## (0.208)
## as.factor(id)11:year_month_id 2.330 ***
## (0.123)
## as.factor(id)12:year_month_id 3.416 ***
## (0.131)
## as.factor(id)13:year_month_id -0.403
## (0.209)
## as.factor(id)14:year_month_id 1.200 ***
## (0.227)
## as.factor(id)15:year_month_id 3.005 ***
## (0.158)
## as.factor(id)16:year_month_id 3.449 ***
## (0.180)
## as.factor(id)17:year_month_id 1.920 ***
## (0.175)
## as.factor(id)18:year_month_id 3.594 ***
## (0.201)
## as.factor(id)19:year_month_id 3.196 ***
## (0.202)
## as.factor(id)20:year_month_id 2.852 ***
## (0.205)
## as.factor(id)21:year_month_id 2.483 ***
## (0.188)
## as.factor(id)22:year_month_id 3.271 ***
## (0.199)
## as.factor(id)23:year_month_id 1.541 ***
## (0.164)
## as.factor(id)24:year_month_id 1.953 ***
## (0.199)
## as.factor(id)25:year_month_id 1.910 ***
## (0.225)
## as.factor(id)26:year_month_id -0.843 ***
## (0.198)
## as.factor(id)27:year_month_id -2.020 ***
## (0.215)
## as.factor(id)28:year_month_id 0.469 **
## (0.142)
## as.factor(id)29:year_month_id -0.138
## (0.224)
## as.factor(id)30:year_month_id 1.549 ***
## (0.178)
## as.factor(id)31:year_month_id 0.814 ***
## (0.186)
## as.factor(id)32:year_month_id 1.861 ***
## (0.181)
## as.factor(id)33:year_month_id 1.006 ***
## (0.181)
## as.factor(id)34:year_month_id 0.231
## (0.179)
## as.factor(id)35:year_month_id 0.559 *
## (0.212)
## as.factor(id)36:year_month_id 0.555 *
## (0.215)
## as.factor(id)37:year_month_id 2.085 ***
## (0.211)
## as.factor(id)38:year_month_id 1.657 ***
## (0.157)
## as.factor(id)39:year_month_id 0.180
## (0.158)
## as.factor(id)40:year_month_id -0.480 **
## (0.161)
## as.factor(id)41:year_month_id 3.156 ***
## (0.187)
## as.factor(id)42:year_month_id 0.700 ***
## (0.198)
## as.factor(id)43:year_month_id 2.210 ***
## (0.156)
## as.factor(id)44:year_month_id 2.866 ***
## (0.158)
## as.factor(id)45:year_month_id 2.929 ***
## (0.191)
## as.factor(id)46:year_month_id 2.055 ***
## (0.164)
## as.factor(id)47:year_month_id 5.085 ***
## (0.260)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 4.409
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_OLS_trend")
# Event study graph
graph_hogo_persons_OLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_OLS_trend")
graph_hogo_persons_OLS_trend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_OLS_trend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 6.589
## (3.336)
## treat_var:date_2018_03 14.412 *
## (6.304)
## treat_var:date_2018_04 14.965
## (7.481)
## treat_var:date_2018_05 21.189 *
## (9.121)
## treat_var:date_2018_06 20.264
## (12.335)
## treat_var:date_2018_07 34.350 **
## (11.608)
## treat_var:date_2018_08 46.211 ***
## (12.600)
## treat_var:date_2018_09 47.207 **
## (13.938)
## treat_var:date_2018_10 61.779 ***
## (13.250)
## treat_var:date_2018_11 59.442 ***
## (15.470)
## treat_var:date_2018_12 53.942 ***
## (15.304)
## treat_var:date_2019_01 3.070
## (7.631)
## treat_var:date_2019_02 2.438
## (7.650)
## treat_var:date_2019_03 4.627
## (9.226)
## treat_var:date_2019_04 1.217
## (9.737)
## treat_var:date_2019_05 10.619
## (11.932)
## treat_var:date_2019_06 13.848
## (13.706)
## treat_var:date_2019_07 21.141
## (13.023)
## treat_var:date_2019_08 26.104
## (14.713)
## treat_var:date_2019_09 29.508 *
## (13.709)
## treat_var:date_2019_10 34.678 *
## (14.265)
## treat_var:date_2019_11 36.059 *
## (13.824)
## treat_var:date_2019_12 45.604 **
## (13.397)
## treat_var:date_2020_02 63.074 **
## (18.780)
## treat_var:date_2020_03 81.183 **
## (23.252)
## treat_var:date_2020_04 80.929 ***
## (22.016)
## treat_var:date_2020_05 72.065 ***
## (18.081)
## treat_var:date_2020_06 71.162 ***
## (18.287)
## treat_var:date_2020_07 74.927 ***
## (19.603)
## treat_var:date_2020_08 70.108 ***
## (19.934)
## treat_var:date_2020_09 74.723 **
## (23.578)
## date_2020_02:google_mobility_index_2020may 1.074
## (0.624)
## date_2020_03:google_mobility_index_2020may 1.351
## (0.815)
## date_2020_04:google_mobility_index_2020may 1.272
## (0.688)
## date_2020_05:google_mobility_index_2020may 0.497
## (0.546)
## date_2020_06:google_mobility_index_2020may -0.039
## (0.613)
## date_2020_07:google_mobility_index_2020may -0.079
## (0.695)
## date_2020_08:google_mobility_index_2020may -0.566
## (0.706)
## date_2020_09:google_mobility_index_2020may -0.681
## (0.790)
## date_2020_02:infection_rate_cumulative2020jun -0.192
## (0.293)
## date_2020_03:infection_rate_cumulative2020jun -0.454
## (0.402)
## date_2020_04:infection_rate_cumulative2020jun -0.233
## (0.312)
## date_2020_05:infection_rate_cumulative2020jun -0.561
## (0.318)
## date_2020_06:infection_rate_cumulative2020jun -0.695
## (0.379)
## date_2020_07:infection_rate_cumulative2020jun -0.855
## (0.442)
## date_2020_08:infection_rate_cumulative2020jun -0.956
## (0.493)
## date_2020_09:infection_rate_cumulative2020jun -1.095 *
## (0.520)
## date_2020_02:death_rate_cumulative2020jun 5.775
## (4.151)
## date_2020_03:death_rate_cumulative2020jun 9.192
## (5.903)
## date_2020_04:death_rate_cumulative2020jun 6.354
## (4.521)
## date_2020_05:death_rate_cumulative2020jun 7.636
## (3.872)
## date_2020_06:death_rate_cumulative2020jun 7.473
## (4.261)
## date_2020_07:death_rate_cumulative2020jun 9.262
## (5.042)
## date_2020_08:death_rate_cumulative2020jun 8.679
## (5.524)
## date_2020_09:death_rate_cumulative2020jun 10.427
## (5.883)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.002)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_02:Secondary_industry_ratio -66.755
## (48.325)
## date_2020_03:Secondary_industry_ratio -106.693
## (60.712)
## date_2020_04:Secondary_industry_ratio -51.937
## (52.310)
## date_2020_05:Secondary_industry_ratio -10.346
## (45.766)
## date_2020_06:Secondary_industry_ratio 54.010
## (53.846)
## date_2020_07:Secondary_industry_ratio 65.617
## (55.346)
## date_2020_08:Secondary_industry_ratio 96.546
## (57.095)
## date_2020_09:Secondary_industry_ratio 109.032
## (66.698)
## date_2020_02:Tertiary_industry_ratio -20.257
## (46.869)
## date_2020_03:Tertiary_industry_ratio -28.247
## (65.196)
## date_2020_04:Tertiary_industry_ratio -23.869
## (55.466)
## date_2020_05:Tertiary_industry_ratio 9.728
## (54.881)
## date_2020_06:Tertiary_industry_ratio 55.388
## (63.991)
## date_2020_07:Tertiary_industry_ratio 44.354
## (66.754)
## date_2020_08:Tertiary_industry_ratio 66.745
## (71.081)
## date_2020_09:Tertiary_industry_ratio 79.912
## (75.670)
## date_2020_02:Total_population 0.005
## (0.007)
## date_2020_03:Total_population 0.007
## (0.010)
## date_2020_04:Total_population 0.008
## (0.008)
## date_2020_05:Total_population 0.003
## (0.006)
## date_2020_06:Total_population 0.008
## (0.007)
## date_2020_07:Total_population 0.007
## (0.007)
## date_2020_08:Total_population 0.009
## (0.007)
## date_2020_09:Total_population 0.011
## (0.008)
## date_2020_02:Ratio_of_aged_population 0.212
## (0.255)
## date_2020_03:Ratio_of_aged_population 0.204
## (0.301)
## date_2020_04:Ratio_of_aged_population 0.120
## (0.262)
## date_2020_05:Ratio_of_aged_population -0.085
## (0.230)
## date_2020_06:Ratio_of_aged_population 0.049
## (0.284)
## date_2020_07:Ratio_of_aged_population 0.034
## (0.293)
## date_2020_08:Ratio_of_aged_population 0.054
## (0.297)
## date_2020_09:Ratio_of_aged_population 0.139
## (0.343)
## as.factor(id)1:year_month_id -0.760 ***
## (0.167)
## as.factor(id)2:year_month_id 1.966 ***
## (0.062)
## as.factor(id)3:year_month_id 2.254 ***
## (0.120)
## as.factor(id)4:year_month_id 3.555 ***
## (0.159)
## as.factor(id)5:year_month_id 1.081 ***
## (0.102)
## as.factor(id)6:year_month_id 3.331 ***
## (0.099)
## as.factor(id)7:year_month_id 3.322 ***
## (0.157)
## as.factor(id)8:year_month_id 2.806 ***
## (0.113)
## as.factor(id)9:year_month_id 1.113 ***
## (0.123)
## as.factor(id)10:year_month_id 1.949 ***
## (0.146)
## as.factor(id)11:year_month_id 1.636 ***
## (0.133)
## as.factor(id)12:year_month_id 2.696 ***
## (0.166)
## as.factor(id)13:year_month_id -1.023 ***
## (0.133)
## as.factor(id)14:year_month_id 0.467 ***
## (0.121)
## as.factor(id)15:year_month_id 2.239 ***
## (0.112)
## as.factor(id)16:year_month_id 2.670 ***
## (0.172)
## as.factor(id)17:year_month_id 1.236 ***
## (0.188)
## as.factor(id)18:year_month_id 2.786 ***
## (0.153)
## as.factor(id)19:year_month_id 2.586 ***
## (0.159)
## as.factor(id)20:year_month_id 2.302 ***
## (0.176)
## as.factor(id)21:year_month_id 1.723 ***
## (0.122)
## as.factor(id)22:year_month_id 2.576 ***
## (0.139)
## as.factor(id)23:year_month_id 0.792 ***
## (0.124)
## as.factor(id)24:year_month_id 1.251 ***
## (0.124)
## as.factor(id)25:year_month_id 1.168 ***
## (0.153)
## as.factor(id)26:year_month_id -1.519 ***
## (0.125)
## as.factor(id)27:year_month_id -2.783 ***
## (0.091)
## as.factor(id)28:year_month_id -0.306 *
## (0.135)
## as.factor(id)29:year_month_id -0.987 ***
## (0.133)
## as.factor(id)30:year_month_id 0.716 ***
## (0.062)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 1.046 ***
## (0.083)
## as.factor(id)33:year_month_id 0.329 **
## (0.109)
## as.factor(id)34:year_month_id -0.535 ***
## (0.126)
## as.factor(id)35:year_month_id -0.192
## (0.144)
## as.factor(id)36:year_month_id -0.162
## (0.096)
## as.factor(id)37:year_month_id 1.400 ***
## (0.127)
## as.factor(id)38:year_month_id 0.927 ***
## (0.063)
## as.factor(id)39:year_month_id -0.465 ***
## (0.120)
## as.factor(id)40:year_month_id -1.236 ***
## (0.162)
## as.factor(id)41:year_month_id 2.539 ***
## (0.145)
## as.factor(id)42:year_month_id 0.003
## (0.120)
## as.factor(id)43:year_month_id 1.444 ***
## (0.048)
## as.factor(id)44:year_month_id 2.063 ***
## (0.056)
## as.factor(id)45:year_month_id 2.055 ***
## (0.067)
## as.factor(id)46:year_month_id 1.312 ***
## (0.087)
## as.factor(id)47:year_month_id 4.407 ***
## (0.189)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 220.667
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_persons_WLS_trend")
# Event study graph
graph_hogo_persons_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_persons_WLS_trend")
graph_hogo_persons_WLS_trend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_persons_WLS_trend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 24.916
## (24.436)
## treat_var:date_2018_02 24.889
## (23.755)
## treat_var:date_2018_03 26.812
## (21.414)
## treat_var:date_2018_04 19.535
## (20.535)
## treat_var:date_2018_05 10.842
## (18.553)
## treat_var:date_2018_06 8.313
## (17.865)
## treat_var:date_2018_07 13.039
## (18.000)
## treat_var:date_2018_08 33.036
## (25.822)
## treat_var:date_2018_09 17.590
## (15.505)
## treat_var:date_2018_10 21.937
## (15.151)
## treat_var:date_2018_11 15.921
## (15.084)
## treat_var:date_2018_12 8.589
## (15.130)
## treat_var:date_2019_01 4.386
## (14.514)
## treat_var:date_2019_02 -1.559
## (11.716)
## treat_var:date_2019_03 -6.123
## (11.916)
## treat_var:date_2019_04 -10.688
## (12.110)
## treat_var:date_2019_05 -3.451
## (13.305)
## treat_var:date_2019_06 -1.709
## (12.010)
## treat_var:date_2019_07 -6.961
## (10.702)
## treat_var:date_2019_08 -11.020
## (10.796)
## treat_var:date_2019_09 -14.650
## (7.508)
## treat_var:date_2019_10 -22.751 *
## (8.737)
## treat_var:date_2019_11 -19.673 *
## (7.368)
## treat_var:date_2019_12 -5.559
## (5.204)
## treat_var:date_2020_02 12.716 **
## (4.253)
## treat_var:date_2020_03 17.247 **
## (6.104)
## treat_var:date_2020_04 34.995 ***
## (8.897)
## treat_var:date_2020_05 44.698 ***
## (8.211)
## treat_var:date_2020_06 59.663 ***
## (10.337)
## treat_var:date_2020_07 66.066 ***
## (11.963)
## treat_var:date_2020_08 66.915 ***
## (14.109)
## treat_var:date_2020_09 73.603 ***
## (16.634)
## ------------------------------------
## R^2 0.870
## Adj. R^2 0.860
## Num. obs. 1551
## RMSE 7.079
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_OLS_notrend")
# Event study graph
graph_yoy_hogo_persons_OLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_OLS_notrend")
graph_yoy_hogo_persons_OLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_OLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 33.478
## (25.388)
## treat_var:date_2018_02 33.775
## (24.502)
## treat_var:date_2018_03 34.073
## (21.949)
## treat_var:date_2018_04 24.831
## (19.557)
## treat_var:date_2018_05 20.029
## (19.427)
## treat_var:date_2018_06 11.236
## (19.086)
## treat_var:date_2018_07 20.442
## (20.095)
## treat_var:date_2018_08 32.466
## (19.823)
## treat_var:date_2018_09 20.135
## (16.101)
## treat_var:date_2018_10 23.680
## (15.708)
## treat_var:date_2018_11 15.931
## (16.104)
## treat_var:date_2018_12 10.076
## (14.430)
## treat_var:date_2019_01 6.174
## (14.683)
## treat_var:date_2019_02 -1.049
## (12.195)
## treat_var:date_2019_03 -6.687
## (11.872)
## treat_var:date_2019_04 -10.643
## (13.338)
## treat_var:date_2019_05 -7.484
## (14.082)
## treat_var:date_2019_06 -3.315
## (12.604)
## treat_var:date_2019_07 -10.124
## (10.185)
## treat_var:date_2019_08 -17.026
## (9.194)
## treat_var:date_2019_09 -14.592
## (7.827)
## treat_var:date_2019_10 -24.000 **
## (7.469)
## treat_var:date_2019_11 -20.272 **
## (6.157)
## treat_var:date_2019_12 -5.231
## (5.128)
## treat_var:date_2020_02 16.316 ***
## (4.220)
## treat_var:date_2020_03 23.707 **
## (6.755)
## treat_var:date_2020_04 45.399 ***
## (8.606)
## treat_var:date_2020_05 58.161 ***
## (8.400)
## treat_var:date_2020_06 81.104 ***
## (11.751)
## treat_var:date_2020_07 85.123 ***
## (13.145)
## treat_var:date_2020_08 89.970 ***
## (14.863)
## treat_var:date_2020_09 94.071 ***
## (15.534)
## ------------------------------------
## R^2 0.931
## Adj. R^2 0.926
## Num. obs. 1551
## RMSE 291.570
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_WLS_notrend")
# Event study graph
graph_yoy_hogo_persons_WLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_WLS_notrend")
graph_yoy_hogo_persons_WLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_WLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 1.011
## (5.981)
## treat_var:date_2018_03 3.972
## (8.142)
## treat_var:date_2018_04 -2.267
## (9.706)
## treat_var:date_2018_05 -9.921
## (10.541)
## treat_var:date_2018_06 -11.413
## (8.383)
## treat_var:date_2018_07 -5.648
## (9.840)
## treat_var:date_2018_08 15.387
## (18.786)
## treat_var:date_2018_09 0.980
## (11.408)
## treat_var:date_2018_10 6.365
## (13.215)
## treat_var:date_2018_11 1.387
## (13.442)
## treat_var:date_2018_12 -4.908
## (12.953)
## treat_var:date_2019_01 -8.072
## (12.622)
## treat_var:date_2019_02 -12.979
## (10.110)
## treat_var:date_2019_03 -16.504
## (9.405)
## treat_var:date_2019_04 -20.032
## (12.998)
## treat_var:date_2019_05 -11.756
## (11.568)
## treat_var:date_2019_06 -8.976
## (10.730)
## treat_var:date_2019_07 -13.190
## (10.053)
## treat_var:date_2019_08 -16.211
## (9.203)
## treat_var:date_2019_09 -18.802 *
## (7.360)
## treat_var:date_2019_10 -25.865 **
## (8.169)
## treat_var:date_2019_11 -21.750 **
## (6.859)
## treat_var:date_2019_12 -6.597
## (5.271)
## treat_var:date_2020_02 13.754 **
## (4.727)
## treat_var:date_2020_03 19.324 **
## (7.004)
## treat_var:date_2020_04 38.109 ***
## (10.236)
## treat_var:date_2020_05 48.851 ***
## (9.852)
## treat_var:date_2020_06 64.854 ***
## (12.031)
## treat_var:date_2020_07 72.295 ***
## (15.536)
## treat_var:date_2020_08 74.183 ***
## (17.412)
## treat_var:date_2020_09 81.908 ***
## (22.337)
## as.factor(id)1:year_month_id 0.806 ***
## (0.103)
## as.factor(id)2:year_month_id 0.196 ***
## (0.032)
## as.factor(id)3:year_month_id 0.589 ***
## (0.056)
## as.factor(id)4:year_month_id 0.286 ***
## (0.037)
## as.factor(id)5:year_month_id 0.012
## (0.055)
## as.factor(id)6:year_month_id 0.247 ***
## (0.058)
## as.factor(id)7:year_month_id
##
## as.factor(id)8:year_month_id -0.464 ***
## (0.106)
## as.factor(id)9:year_month_id 0.932 ***
## (0.106)
## as.factor(id)10:year_month_id 0.383 ***
## (0.102)
## as.factor(id)11:year_month_id 0.672 ***
## (0.081)
## as.factor(id)12:year_month_id 0.147
## (0.132)
## as.factor(id)13:year_month_id 0.213
## (0.110)
## as.factor(id)14:year_month_id 0.455 ***
## (0.098)
## as.factor(id)15:year_month_id 0.023
## (0.079)
## as.factor(id)16:year_month_id 0.522 ***
## (0.119)
## as.factor(id)17:year_month_id 0.744 ***
## (0.069)
## as.factor(id)18:year_month_id 1.015 ***
## (0.058)
## as.factor(id)19:year_month_id 0.136
## (0.081)
## as.factor(id)20:year_month_id 0.600 ***
## (0.060)
## as.factor(id)21:year_month_id 0.270
## (0.164)
## as.factor(id)22:year_month_id 0.602 ***
## (0.140)
## as.factor(id)23:year_month_id 0.746 ***
## (0.155)
## as.factor(id)24:year_month_id 0.869 ***
## (0.113)
## as.factor(id)25:year_month_id 0.510 ***
## (0.124)
## as.factor(id)26:year_month_id 0.178
## (0.141)
## as.factor(id)27:year_month_id 0.351 **
## (0.126)
## as.factor(id)28:year_month_id 0.166
## (0.116)
## as.factor(id)29:year_month_id -1.050 ***
## (0.107)
## as.factor(id)30:year_month_id -0.882 ***
## (0.138)
## as.factor(id)31:year_month_id -0.077
## (0.057)
## as.factor(id)32:year_month_id 0.390 ***
## (0.109)
## as.factor(id)33:year_month_id 0.444 ***
## (0.041)
## as.factor(id)34:year_month_id 0.986 ***
## (0.122)
## as.factor(id)35:year_month_id 0.435 ***
## (0.058)
## as.factor(id)36:year_month_id 0.747 ***
## (0.068)
## as.factor(id)37:year_month_id 0.282 ***
## (0.055)
## as.factor(id)38:year_month_id 0.448 ***
## (0.064)
## as.factor(id)39:year_month_id 0.872 ***
## (0.006)
## as.factor(id)40:year_month_id 0.865 ***
## (0.101)
## as.factor(id)41:year_month_id 0.666 ***
## (0.035)
## as.factor(id)42:year_month_id 0.535 ***
## (0.032)
## as.factor(id)43:year_month_id 1.551 ***
## (0.067)
## as.factor(id)44:year_month_id 1.185 ***
## (0.099)
## as.factor(id)45:year_month_id 0.709 ***
## (0.127)
## as.factor(id)46:year_month_id 1.531 ***
## (0.001)
## as.factor(id)47:year_month_id 0.757 ***
## (0.109)
## -------------------------------------------
## R^2 0.929
## Adj. R^2 0.921
## Num. obs. 1551
## RMSE 5.305
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_OLS_trend")
# Event study graph
graph_yoy_hogo_persons_OLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_OLS_trend")
graph_yoy_hogo_persons_OLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_OLS_trend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 1.691
## (5.341)
## treat_var:date_2018_03 3.383
## (6.735)
## treat_var:date_2018_04 -4.465
## (8.938)
## treat_var:date_2018_05 -7.872
## (9.178)
## treat_var:date_2018_06 -15.271
## (8.237)
## treat_var:date_2018_07 -4.671
## (9.386)
## treat_var:date_2018_08 8.747
## (11.840)
## treat_var:date_2018_09 -2.190
## (10.259)
## treat_var:date_2018_10 2.749
## (11.205)
## treat_var:date_2018_11 -3.605
## (11.994)
## treat_var:date_2018_12 -8.067
## (11.543)
## treat_var:date_2019_01 -10.579
## (11.078)
## treat_var:date_2019_02 -16.407
## (9.161)
## treat_var:date_2019_03 -20.649 *
## (8.859)
## treat_var:date_2019_04 -23.209 *
## (11.005)
## treat_var:date_2019_05 -18.655
## (10.962)
## treat_var:date_2019_06 -13.091
## (9.888)
## treat_var:date_2019_07 -18.504 *
## (8.471)
## treat_var:date_2019_08 -24.011 **
## (7.149)
## treat_var:date_2019_09 -20.182 **
## (6.439)
## treat_var:date_2019_10 -28.195 ***
## (6.705)
## treat_var:date_2019_11 -23.071 ***
## (6.147)
## treat_var:date_2019_12 -6.635
## (5.204)
## treat_var:date_2020_02 17.712 ***
## (4.758)
## treat_var:date_2020_03 26.500 **
## (7.834)
## treat_var:date_2020_04 49.589 ***
## (10.071)
## treat_var:date_2020_05 63.747 ***
## (10.149)
## treat_var:date_2020_06 88.086 ***
## (13.498)
## treat_var:date_2020_07 93.501 ***
## (15.721)
## treat_var:date_2020_08 99.744 ***
## (17.316)
## treat_var:date_2020_09 105.242 ***
## (19.659)
## as.factor(id)1:year_month_id 0.422 ***
## (0.005)
## as.factor(id)2:year_month_id -0.119
## (0.070)
## as.factor(id)3:year_month_id 0.369 *
## (0.148)
## as.factor(id)4:year_month_id -0.029
## (0.065)
## as.factor(id)5:year_month_id -0.314 ***
## (0.048)
## as.factor(id)6:year_month_id -0.092
## (0.046)
## as.factor(id)7:year_month_id -0.276 **
## (0.098)
## as.factor(id)8:year_month_id -0.850 ***
## (0.003)
## as.factor(id)9:year_month_id 0.544 ***
## (0.003)
## as.factor(id)10:year_month_id 0.001
## (0.007)
## as.factor(id)11:year_month_id 0.310 ***
## (0.026)
## as.factor(id)12:year_month_id -0.268 ***
## (0.021)
## as.factor(id)13:year_month_id -0.178 ***
## (0.002)
## as.factor(id)14:year_month_id 0.077 ***
## (0.010)
## as.factor(id)15:year_month_id -0.336 ***
## (0.027)
## as.factor(id)16:year_month_id 0.122 ***
## (0.009)
## as.factor(id)17:year_month_id 0.395 ***
## (0.036)
## as.factor(id)18:year_month_id 0.678 ***
## (0.046)
## as.factor(id)19:year_month_id -0.225 ***
## (0.026)
## as.factor(id)20:year_month_id 0.261 ***
## (0.044)
## as.factor(id)21:year_month_id -0.177 ***
## (0.050)
## as.factor(id)22:year_month_id 0.180 ***
## (0.028)
## as.factor(id)23:year_month_id 0.307 ***
## (0.042)
## as.factor(id)24:year_month_id 0.475 ***
## (0.003)
## as.factor(id)25:year_month_id 0.103 ***
## (0.013)
## as.factor(id)26:year_month_id -0.247 ***
## (0.029)
## as.factor(id)27:year_month_id -0.057 ***
## (0.015)
## as.factor(id)28:year_month_id -0.233 ***
## (0.007)
## as.factor(id)29:year_month_id -1.438 ***
## (0.002)
## as.factor(id)30:year_month_id -1.303 ***
## (0.026)
## as.factor(id)31:year_month_id -0.411 ***
## (0.047)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id 0.123 *
## (0.061)
## as.factor(id)34:year_month_id 0.581 ***
## (0.012)
## as.factor(id)35:year_month_id 0.099 *
## (0.046)
## as.factor(id)36:year_month_id 0.394 ***
## (0.037)
## as.factor(id)37:year_month_id -0.054
## (0.049)
## as.factor(id)38:year_month_id 0.105 *
## (0.041)
## as.factor(id)39:year_month_id 0.601 ***
## (0.103)
## as.factor(id)40:year_month_id 0.483 ***
## (0.007)
## as.factor(id)41:year_month_id 0.425 **
## (0.130)
## as.factor(id)42:year_month_id 0.293 *
## (0.128)
## as.factor(id)43:year_month_id 1.204 ***
## (0.038)
## as.factor(id)44:year_month_id 0.809 ***
## (0.009)
## as.factor(id)45:year_month_id 0.305 ***
## (0.017)
## as.factor(id)46:year_month_id 1.256 ***
## (0.099)
## as.factor(id)47:year_month_id 0.365 ***
## (0.000)
## -------------------------------------------
## R^2 0.965
## Adj. R^2 0.961
## Num. obs. 1551
## RMSE 211.461
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_WLS_trend")
# Event study graph
graph_yoy_hogo_persons_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_WLS_trend")
graph_yoy_hogo_persons_WLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_WLS_trend <- df_estimates #for robustness check
results_yoy_hogo_persons_WLS_trend <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_WLS_trend")
# Event study graph
graph_yoy_hogo_persons_WLS_trend_onlypost <- event_study_graph(data = df_estimates ,
graph_title = "yoy_hogo_persons_WLS_trend")
ggplotly(graph_yoy_hogo_persons_WLS_trend_onlypost)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
estimates_yoy_hogo_persons_WLS_trend_onlypost <- df_estimates #for robustness check
results_yoy_hogo_persons_WLS_trend_onlypost <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ==================================================================
## Model 1
## ------------------------------------------------------------------
## treat_var:date_2018_01 24.916
## (24.998)
## treat_var:date_2018_02 24.889
## (24.301)
## treat_var:date_2018_03 26.812
## (21.906)
## treat_var:date_2018_04 19.535
## (21.007)
## treat_var:date_2018_05 10.842
## (18.979)
## treat_var:date_2018_06 8.313
## (18.276)
## treat_var:date_2018_07 13.039
## (18.414)
## treat_var:date_2018_08 33.036
## (26.416)
## treat_var:date_2018_09 17.590
## (15.862)
## treat_var:date_2018_10 21.937
## (15.499)
## treat_var:date_2018_11 15.921
## (15.431)
## treat_var:date_2018_12 8.589
## (15.478)
## treat_var:date_2019_01 4.386
## (14.848)
## treat_var:date_2019_02 -1.559
## (11.986)
## treat_var:date_2019_03 -6.123
## (12.190)
## treat_var:date_2019_04 -10.688
## (12.389)
## treat_var:date_2019_05 -3.451
## (13.611)
## treat_var:date_2019_06 -1.709
## (12.286)
## treat_var:date_2019_07 -6.961
## (10.948)
## treat_var:date_2019_08 -11.020
## (11.044)
## treat_var:date_2019_09 -14.650
## (7.680)
## treat_var:date_2019_10 -22.751 *
## (8.938)
## treat_var:date_2019_11 -19.673 *
## (7.538)
## treat_var:date_2019_12 -5.559
## (5.324)
## treat_var:date_2020_02 17.103
## (13.166)
## treat_var:date_2020_03 23.423
## (14.775)
## treat_var:date_2020_04 32.417 *
## (14.768)
## treat_var:date_2020_05 30.994 *
## (12.475)
## treat_var:date_2020_06 30.789 *
## (13.896)
## treat_var:date_2020_07 32.340 *
## (15.289)
## treat_var:date_2020_08 24.759
## (18.959)
## treat_var:date_2020_09 26.431
## (21.640)
## date_2020_02:google_mobility_index_2020may 0.470
## (0.570)
## date_2020_03:google_mobility_index_2020may 0.493
## (0.576)
## date_2020_04:google_mobility_index_2020may 0.157
## (0.589)
## date_2020_05:google_mobility_index_2020may -0.185
## (0.717)
## date_2020_06:google_mobility_index_2020may -0.770
## (0.725)
## date_2020_07:google_mobility_index_2020may -0.924
## (0.856)
## date_2020_08:google_mobility_index_2020may -1.763 *
## (0.859)
## date_2020_09:google_mobility_index_2020may -1.652
## (0.944)
## date_2020_02:infection_rate_cumulative2020jun 0.208
## (0.426)
## date_2020_03:infection_rate_cumulative2020jun -0.025
## (0.466)
## date_2020_04:infection_rate_cumulative2020jun -0.085
## (0.476)
## date_2020_05:infection_rate_cumulative2020jun -0.576
## (0.621)
## date_2020_06:infection_rate_cumulative2020jun -0.686
## (0.640)
## date_2020_07:infection_rate_cumulative2020jun -0.917
## (0.769)
## date_2020_08:infection_rate_cumulative2020jun -1.347
## (0.814)
## date_2020_09:infection_rate_cumulative2020jun -1.450
## (0.859)
## date_2020_02:death_rate_cumulative2020jun 0.483
## (4.131)
## date_2020_03:death_rate_cumulative2020jun 1.787
## (4.563)
## date_2020_04:death_rate_cumulative2020jun 1.784
## (4.823)
## date_2020_05:death_rate_cumulative2020jun 6.504
## (6.202)
## date_2020_06:death_rate_cumulative2020jun 7.425
## (6.372)
## date_2020_07:death_rate_cumulative2020jun 10.927
## (7.256)
## date_2020_08:death_rate_cumulative2020jun 14.409
## (7.916)
## date_2020_09:death_rate_cumulative2020jun 15.426
## (8.229)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.002
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.002
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.002)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.002)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.002)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.002)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.002)
## date_2020_02:Secondary_industry_ratio -40.509
## (52.945)
## date_2020_03:Secondary_industry_ratio -63.892
## (59.105)
## date_2020_04:Secondary_industry_ratio -65.476
## (59.664)
## date_2020_05:Secondary_industry_ratio -54.174
## (65.491)
## date_2020_06:Secondary_industry_ratio -15.055
## (66.125)
## date_2020_07:Secondary_industry_ratio 9.484
## (67.636)
## date_2020_08:Secondary_industry_ratio 33.345
## (66.017)
## date_2020_09:Secondary_industry_ratio 69.245
## (70.577)
## date_2020_02:Tertiary_industry_ratio -51.008
## (77.721)
## date_2020_03:Tertiary_industry_ratio -64.550
## (88.162)
## date_2020_04:Tertiary_industry_ratio -68.093
## (89.717)
## date_2020_05:Tertiary_industry_ratio -36.401
## (103.244)
## date_2020_06:Tertiary_industry_ratio -6.194
## (102.742)
## date_2020_07:Tertiary_industry_ratio -24.642
## (100.762)
## date_2020_08:Tertiary_industry_ratio -0.370
## (99.567)
## date_2020_09:Tertiary_industry_ratio 9.080
## (96.347)
## date_2020_02:Total_population 0.005
## (0.008)
## date_2020_03:Total_population 0.008
## (0.008)
## date_2020_04:Total_population 0.010
## (0.009)
## date_2020_05:Total_population 0.007
## (0.010)
## date_2020_06:Total_population 0.012
## (0.010)
## date_2020_07:Total_population 0.010
## (0.010)
## date_2020_08:Total_population 0.010
## (0.010)
## date_2020_09:Total_population 0.014
## (0.011)
## date_2020_02:Ratio_of_aged_population -0.286
## (0.213)
## date_2020_03:Ratio_of_aged_population -0.336
## (0.223)
## date_2020_04:Ratio_of_aged_population -0.378
## (0.263)
## date_2020_05:Ratio_of_aged_population -0.530
## (0.290)
## date_2020_06:Ratio_of_aged_population -0.213
## (0.314)
## date_2020_07:Ratio_of_aged_population -0.085
## (0.357)
## date_2020_08:Ratio_of_aged_population 0.274
## (0.389)
## date_2020_09:Ratio_of_aged_population 0.300
## (0.418)
## ------------------------------------------------------------------
## R^2 0.880
## Adj. R^2 0.865
## Num. obs. 1551
## RMSE 6.954
## N Clusters 47
## ==================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_OLS_notrend")
# Event study graph
graph_yoy_hogo_persons_OLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_OLS_notrend")
graph_yoy_hogo_persons_OLS_notrend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_OLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ===================================================================
## Model 1
## -------------------------------------------------------------------
## treat_var:date_2018_01 33.473
## (25.979)
## treat_var:date_2018_02 33.770
## (25.073)
## treat_var:date_2018_03 34.068
## (22.460)
## treat_var:date_2018_04 24.825
## (20.013)
## treat_var:date_2018_05 20.023
## (19.882)
## treat_var:date_2018_06 11.231
## (19.534)
## treat_var:date_2018_07 20.436
## (20.566)
## treat_var:date_2018_08 32.460
## (20.287)
## treat_var:date_2018_09 20.129
## (16.478)
## treat_var:date_2018_10 23.674
## (16.076)
## treat_var:date_2018_11 15.926
## (16.484)
## treat_var:date_2018_12 10.070
## (14.770)
## treat_var:date_2019_01 6.170
## (15.024)
## treat_var:date_2019_02 -1.052
## (12.478)
## treat_var:date_2019_03 -6.690
## (12.149)
## treat_var:date_2019_04 -10.646
## (13.650)
## treat_var:date_2019_05 -7.487
## (14.410)
## treat_var:date_2019_06 -3.318
## (12.898)
## treat_var:date_2019_07 -10.127
## (10.423)
## treat_var:date_2019_08 -17.029
## (9.408)
## treat_var:date_2019_09 -14.595
## (8.011)
## treat_var:date_2019_10 -24.003 **
## (7.644)
## treat_var:date_2019_11 -20.275 **
## (6.298)
## treat_var:date_2019_12 -5.234
## (5.244)
## treat_var:date_2020_02 26.018
## (14.189)
## treat_var:date_2020_03 37.345 *
## (16.331)
## treat_var:date_2020_04 53.787 **
## (17.601)
## treat_var:date_2020_05 54.726 **
## (16.880)
## treat_var:date_2020_06 62.910 **
## (20.025)
## treat_var:date_2020_07 59.710 **
## (20.134)
## treat_var:date_2020_08 54.637 *
## (22.338)
## treat_var:date_2020_09 54.863 *
## (24.227)
## date_2020_02:google_mobility_index_2020may 0.927
## (0.535)
## date_2020_03:google_mobility_index_2020may 1.050
## (0.620)
## date_2020_04:google_mobility_index_2020may 1.172
## (0.600)
## date_2020_05:google_mobility_index_2020may 0.775
## (0.715)
## date_2020_06:google_mobility_index_2020may 0.483
## (0.774)
## date_2020_07:google_mobility_index_2020may 0.262
## (0.825)
## date_2020_08:google_mobility_index_2020may -0.281
## (0.841)
## date_2020_09:google_mobility_index_2020may -0.444
## (0.832)
## date_2020_02:infection_rate_cumulative2020jun 0.290
## (0.420)
## date_2020_03:infection_rate_cumulative2020jun 0.029
## (0.502)
## date_2020_04:infection_rate_cumulative2020jun 0.048
## (0.432)
## date_2020_05:infection_rate_cumulative2020jun -0.202
## (0.579)
## date_2020_06:infection_rate_cumulative2020jun -0.474
## (0.604)
## date_2020_07:infection_rate_cumulative2020jun -0.796
## (0.628)
## date_2020_08:infection_rate_cumulative2020jun -0.984
## (0.627)
## date_2020_09:infection_rate_cumulative2020jun -1.274 *
## (0.576)
## date_2020_02:death_rate_cumulative2020jun -1.410
## (4.494)
## date_2020_03:death_rate_cumulative2020jun 0.680
## (5.432)
## date_2020_04:death_rate_cumulative2020jun 0.865
## (4.898)
## date_2020_05:death_rate_cumulative2020jun 2.262
## (6.239)
## date_2020_06:death_rate_cumulative2020jun 5.023
## (6.698)
## date_2020_07:death_rate_cumulative2020jun 9.915
## (7.011)
## date_2020_08:death_rate_cumulative2020jun 11.056
## (7.060)
## date_2020_09:death_rate_cumulative2020jun 14.329 *
## (6.551)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.002)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.002)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_02:Secondary_industry_ratio -27.520
## (47.558)
## date_2020_03:Secondary_industry_ratio -37.727
## (55.186)
## date_2020_04:Secondary_industry_ratio -49.208
## (56.152)
## date_2020_05:Secondary_industry_ratio -36.193
## (65.486)
## date_2020_06:Secondary_industry_ratio -1.841
## (68.267)
## date_2020_07:Secondary_industry_ratio 18.988
## (68.250)
## date_2020_08:Secondary_industry_ratio 46.657
## (67.631)
## date_2020_09:Secondary_industry_ratio 68.721
## (70.923)
## date_2020_02:Tertiary_industry_ratio -22.920
## (67.712)
## date_2020_03:Tertiary_industry_ratio -11.807
## (81.400)
## date_2020_04:Tertiary_industry_ratio -22.711
## (78.546)
## date_2020_05:Tertiary_industry_ratio 11.423
## (95.482)
## date_2020_06:Tertiary_industry_ratio 44.376
## (98.711)
## date_2020_07:Tertiary_industry_ratio 23.865
## (96.289)
## date_2020_08:Tertiary_industry_ratio 41.159
## (93.870)
## date_2020_09:Tertiary_industry_ratio 52.150
## (90.208)
## date_2020_02:Total_population 0.003
## (0.006)
## date_2020_03:Total_population 0.005
## (0.007)
## date_2020_04:Total_population 0.007
## (0.007)
## date_2020_05:Total_population 0.003
## (0.007)
## date_2020_06:Total_population 0.009
## (0.008)
## date_2020_07:Total_population 0.008
## (0.008)
## date_2020_08:Total_population 0.010
## (0.008)
## date_2020_09:Total_population 0.013
## (0.009)
## date_2020_02:Ratio_of_aged_population -0.468 *
## (0.208)
## date_2020_03:Ratio_of_aged_population -0.440
## (0.247)
## date_2020_04:Ratio_of_aged_population -0.609 *
## (0.236)
## date_2020_05:Ratio_of_aged_population -0.728 *
## (0.281)
## date_2020_06:Ratio_of_aged_population -0.517
## (0.300)
## date_2020_07:Ratio_of_aged_population -0.375
## (0.315)
## date_2020_08:Ratio_of_aged_population -0.165
## (0.324)
## date_2020_09:Ratio_of_aged_population 0.031
## (0.335)
## -------------------------------------------------------------------
## R^2 0.938
## Adj. R^2 0.930
## Num. obs. 1551
## RMSE 284.557
## N Clusters 47
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_WLS_notrend")
# Event study graph
graph_yoy_hogo_persons_WLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_WLS_notrend")
graph_yoy_hogo_persons_WLS_notrend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_WLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 1.011
## (6.123)
## treat_var:date_2018_03 3.972
## (8.335)
## treat_var:date_2018_04 -2.267
## (9.937)
## treat_var:date_2018_05 -9.921
## (10.792)
## treat_var:date_2018_06 -11.413
## (8.582)
## treat_var:date_2018_07 -5.648
## (10.074)
## treat_var:date_2018_08 15.387
## (19.232)
## treat_var:date_2018_09 0.980
## (11.679)
## treat_var:date_2018_10 6.365
## (13.529)
## treat_var:date_2018_11 1.387
## (13.761)
## treat_var:date_2018_12 -4.908
## (13.261)
## treat_var:date_2019_01 -8.072
## (12.922)
## treat_var:date_2019_02 -12.979
## (10.350)
## treat_var:date_2019_03 -16.504
## (9.628)
## treat_var:date_2019_04 -20.032
## (13.307)
## treat_var:date_2019_05 -11.756
## (11.843)
## treat_var:date_2019_06 -8.976
## (10.985)
## treat_var:date_2019_07 -13.190
## (10.292)
## treat_var:date_2019_08 -16.211
## (9.421)
## treat_var:date_2019_09 -18.802 *
## (7.535)
## treat_var:date_2019_10 -25.865 **
## (8.363)
## treat_var:date_2019_11 -21.750 **
## (7.022)
## treat_var:date_2019_12 -6.597
## (5.396)
## treat_var:date_2020_02 6.053
## (13.344)
## treat_var:date_2020_03 12.483
## (13.877)
## treat_var:date_2020_04 21.585
## (20.956)
## treat_var:date_2020_05 20.270
## (22.295)
## treat_var:date_2020_06 20.174
## (26.428)
## treat_var:date_2020_07 21.832
## (32.502)
## treat_var:date_2020_08 14.360
## (37.104)
## treat_var:date_2020_09 16.140
## (44.841)
## date_2020_02:google_mobility_index_2020may 0.071
## (0.768)
## date_2020_03:google_mobility_index_2020may 0.063
## (0.809)
## date_2020_04:google_mobility_index_2020may -0.303
## (1.123)
## date_2020_05:google_mobility_index_2020may -0.676
## (1.157)
## date_2020_06:google_mobility_index_2020may -1.292
## (1.320)
## date_2020_07:google_mobility_index_2020may -1.476
## (1.454)
## date_2020_08:google_mobility_index_2020may -2.347
## (1.567)
## date_2020_09:google_mobility_index_2020may -2.266
## (1.792)
## date_2020_02:infection_rate_cumulative2020jun 0.294
## (0.418)
## date_2020_03:infection_rate_cumulative2020jun 0.067
## (0.512)
## date_2020_04:infection_rate_cumulative2020jun 0.013
## (0.628)
## date_2020_05:infection_rate_cumulative2020jun -0.471
## (0.769)
## date_2020_06:infection_rate_cumulative2020jun -0.574
## (0.814)
## date_2020_07:infection_rate_cumulative2020jun -0.799
## (0.977)
## date_2020_08:infection_rate_cumulative2020jun -1.222
## (1.061)
## date_2020_09:infection_rate_cumulative2020jun -1.318
## (1.147)
## date_2020_02:death_rate_cumulative2020jun -2.091
## (3.911)
## date_2020_03:death_rate_cumulative2020jun -0.986
## (4.893)
## date_2020_04:death_rate_cumulative2020jun -1.186
## (5.850)
## date_2020_05:death_rate_cumulative2020jun 3.335
## (7.290)
## date_2020_06:death_rate_cumulative2020jun 4.058
## (7.709)
## date_2020_07:death_rate_cumulative2020jun 7.363
## (9.272)
## date_2020_08:death_rate_cumulative2020jun 10.647
## (10.158)
## date_2020_09:death_rate_cumulative2020jun 11.466
## (11.037)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.002)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.002)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.002)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.003
## (0.002)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.003
## (0.003)
## date_2020_02:Secondary_industry_ratio 19.583
## (51.267)
## date_2020_03:Secondary_industry_ratio 0.821
## (54.073)
## date_2020_04:Secondary_industry_ratio 3.860
## (73.238)
## date_2020_05:Secondary_industry_ratio 19.785
## (72.791)
## date_2020_06:Secondary_industry_ratio 63.526
## (86.522)
## date_2020_07:Secondary_industry_ratio 92.687
## (94.994)
## date_2020_08:Secondary_industry_ratio 121.171
## (102.724)
## date_2020_09:Secondary_industry_ratio 161.693
## (121.243)
## date_2020_02:Tertiary_industry_ratio -8.481
## (50.812)
## date_2020_03:Tertiary_industry_ratio -18.751
## (59.817)
## date_2020_04:Tertiary_industry_ratio -19.024
## (76.206)
## date_2020_05:Tertiary_industry_ratio 15.940
## (80.663)
## date_2020_06:Tertiary_industry_ratio 49.418
## (89.748)
## date_2020_07:Tertiary_industry_ratio 34.241
## (97.006)
## date_2020_08:Tertiary_industry_ratio 61.785
## (106.038)
## date_2020_09:Tertiary_industry_ratio 74.506
## (116.576)
## date_2020_02:Total_population 0.001
## (0.005)
## date_2020_03:Total_population 0.004
## (0.007)
## date_2020_04:Total_population 0.006
## (0.009)
## date_2020_05:Total_population 0.002
## (0.009)
## date_2020_06:Total_population 0.008
## (0.010)
## date_2020_07:Total_population 0.005
## (0.011)
## date_2020_08:Total_population 0.005
## (0.012)
## date_2020_09:Total_population 0.008
## (0.015)
## date_2020_02:Ratio_of_aged_population 0.063
## (0.343)
## date_2020_03:Ratio_of_aged_population 0.039
## (0.372)
## date_2020_04:Ratio_of_aged_population 0.025
## (0.495)
## date_2020_05:Ratio_of_aged_population -0.100
## (0.493)
## date_2020_06:Ratio_of_aged_population 0.243
## (0.560)
## date_2020_07:Ratio_of_aged_population 0.398
## (0.604)
## date_2020_08:Ratio_of_aged_population 0.784
## (0.654)
## date_2020_09:Ratio_of_aged_population 0.837
## (0.738)
## as.factor(id)1:year_month_id
##
## as.factor(id)2:year_month_id -0.510
## (0.261)
## as.factor(id)3:year_month_id -0.463 *
## (0.227)
## as.factor(id)4:year_month_id -0.770 **
## (0.235)
## as.factor(id)5:year_month_id -0.807 ***
## (0.220)
## as.factor(id)6:year_month_id -0.604 **
## (0.173)
## as.factor(id)7:year_month_id -1.087 ***
## (0.213)
## as.factor(id)8:year_month_id -1.457 ***
## (0.167)
## as.factor(id)9:year_month_id -0.033
## (0.182)
## as.factor(id)10:year_month_id -0.742 **
## (0.262)
## as.factor(id)11:year_month_id -0.495 **
## (0.153)
## as.factor(id)12:year_month_id -0.868 ***
## (0.180)
## as.factor(id)13:year_month_id -1.322 ***
## (0.240)
## as.factor(id)14:year_month_id -1.037 ***
## (0.278)
## as.factor(id)15:year_month_id -0.915 ***
## (0.163)
## as.factor(id)16:year_month_id -0.515 *
## (0.216)
## as.factor(id)17:year_month_id -0.349
## (0.227)
## as.factor(id)18:year_month_id 0.099
## (0.197)
## as.factor(id)19:year_month_id -0.889 ***
## (0.247)
## as.factor(id)20:year_month_id -0.534
## (0.283)
## as.factor(id)21:year_month_id -0.677 **
## (0.200)
## as.factor(id)22:year_month_id -0.519 *
## (0.247)
## as.factor(id)23:year_month_id -0.391 *
## (0.170)
## as.factor(id)24:year_month_id -0.189
## (0.227)
## as.factor(id)25:year_month_id -0.400
## (0.211)
## as.factor(id)26:year_month_id -0.818 **
## (0.238)
## as.factor(id)27:year_month_id -0.966 ***
## (0.213)
## as.factor(id)28:year_month_id -0.925 ***
## (0.173)
## as.factor(id)29:year_month_id -1.981 ***
## (0.223)
## as.factor(id)30:year_month_id -1.621 ***
## (0.213)
## as.factor(id)31:year_month_id -0.897 ***
## (0.218)
## as.factor(id)32:year_month_id -0.422
## (0.212)
## as.factor(id)33:year_month_id -0.636 **
## (0.215)
## as.factor(id)34:year_month_id -0.001
## (0.194)
## as.factor(id)35:year_month_id -0.634 *
## (0.272)
## as.factor(id)36:year_month_id -0.264
## (0.280)
## as.factor(id)37:year_month_id -0.811 **
## (0.266)
## as.factor(id)38:year_month_id -0.462 *
## (0.179)
## as.factor(id)39:year_month_id -0.069
## (0.206)
## as.factor(id)40:year_month_id -0.093
## (0.187)
## as.factor(id)41:year_month_id -0.365
## (0.218)
## as.factor(id)42:year_month_id -0.572 *
## (0.256)
## as.factor(id)43:year_month_id 0.670 ***
## (0.183)
## as.factor(id)44:year_month_id 0.402 *
## (0.170)
## as.factor(id)45:year_month_id 0.047
## (0.253)
## as.factor(id)46:year_month_id 0.552 **
## (0.200)
## as.factor(id)47:year_month_id -0.103
## (0.315)
## --------------------------------------------------------------------
## R^2 0.939
## Adj. R^2 0.929
## Num. obs. 1551
## RMSE 5.048
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_OLS_trend")
# Event study graph
graph_yoy_hogo_persons_OLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_OLS_trend")
graph_yoy_hogo_persons_OLS_trend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_OLS_trend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 1.691
## (5.468)
## treat_var:date_2018_03 3.382
## (6.896)
## treat_var:date_2018_04 -4.466
## (9.152)
## treat_var:date_2018_05 -7.874
## (9.397)
## treat_var:date_2018_06 -15.273
## (8.431)
## treat_var:date_2018_07 -4.674
## (9.606)
## treat_var:date_2018_08 8.743
## (12.120)
## treat_var:date_2018_09 -2.194
## (10.501)
## treat_var:date_2018_10 2.745
## (11.471)
## treat_var:date_2018_11 -3.610
## (12.277)
## treat_var:date_2018_12 -8.071
## (11.818)
## treat_var:date_2019_01 -10.583
## (11.340)
## treat_var:date_2019_02 -16.411
## (9.377)
## treat_var:date_2019_03 -20.653 *
## (9.070)
## treat_var:date_2019_04 -23.214 *
## (11.265)
## treat_var:date_2019_05 -18.659
## (11.223)
## treat_var:date_2019_06 -13.096
## (10.125)
## treat_var:date_2019_07 -18.509 *
## (8.677)
## treat_var:date_2019_08 -24.016 **
## (7.324)
## treat_var:date_2019_09 -20.187 **
## (6.598)
## treat_var:date_2019_10 -28.200 ***
## (6.869)
## treat_var:date_2019_11 -23.076 ***
## (6.305)
## treat_var:date_2019_12 -6.640
## (5.333)
## treat_var:date_2020_02 8.029
## (12.920)
## treat_var:date_2020_03 19.259
## (14.922)
## treat_var:date_2020_04 35.604
## (20.382)
## treat_var:date_2020_05 36.446
## (20.923)
## treat_var:date_2020_06 44.533
## (26.517)
## treat_var:date_2020_07 41.235
## (29.851)
## treat_var:date_2020_08 36.065
## (32.992)
## treat_var:date_2020_09 36.194
## (38.259)
## date_2020_02:google_mobility_index_2020may 0.538
## (0.593)
## date_2020_03:google_mobility_index_2020may 0.630
## (0.668)
## date_2020_04:google_mobility_index_2020may 0.722
## (0.842)
## date_2020_05:google_mobility_index_2020may 0.294
## (0.862)
## date_2020_06:google_mobility_index_2020may -0.028
## (1.030)
## date_2020_07:google_mobility_index_2020may -0.279
## (1.101)
## date_2020_08:google_mobility_index_2020may -0.852
## (1.177)
## date_2020_09:google_mobility_index_2020may -1.046
## (1.327)
## date_2020_02:infection_rate_cumulative2020jun 0.428
## (0.324)
## date_2020_03:infection_rate_cumulative2020jun 0.178
## (0.415)
## date_2020_04:infection_rate_cumulative2020jun 0.207
## (0.357)
## date_2020_05:infection_rate_cumulative2020jun -0.033
## (0.480)
## date_2020_06:infection_rate_cumulative2020jun -0.293
## (0.518)
## date_2020_07:infection_rate_cumulative2020jun -0.605
## (0.560)
## date_2020_08:infection_rate_cumulative2020jun -0.783
## (0.570)
## date_2020_09:infection_rate_cumulative2020jun -1.063
## (0.556)
## date_2020_02:death_rate_cumulative2020jun -3.643
## (3.343)
## date_2020_03:death_rate_cumulative2020jun -1.724
## (4.418)
## date_2020_04:death_rate_cumulative2020jun -1.710
## (3.869)
## date_2020_05:death_rate_cumulative2020jun -0.484
## (5.056)
## date_2020_06:death_rate_cumulative2020jun 2.107
## (5.606)
## date_2020_07:death_rate_cumulative2020jun 6.828
## (6.238)
## date_2020_08:death_rate_cumulative2020jun 7.798
## (6.269)
## date_2020_09:death_rate_cumulative2020jun 10.900
## (6.299)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.002)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.003
## (0.002)
## date_2020_02:Secondary_industry_ratio 57.951
## (46.205)
## date_2020_03:Secondary_industry_ratio 54.309
## (52.231)
## date_2020_04:Secondary_industry_ratio 49.394
## (62.805)
## date_2020_05:Secondary_industry_ratio 68.975
## (63.832)
## date_2020_06:Secondary_industry_ratio 109.892
## (77.220)
## date_2020_07:Secondary_industry_ratio 137.288
## (82.563)
## date_2020_08:Secondary_industry_ratio 171.522
## (87.629)
## date_2020_09:Secondary_industry_ratio 200.152
## (100.651)
## date_2020_02:Tertiary_industry_ratio 56.014
## (44.411)
## date_2020_03:Tertiary_industry_ratio 73.189
## (57.960)
## date_2020_04:Tertiary_industry_ratio 68.347
## (58.362)
## date_2020_05:Tertiary_industry_ratio 108.542
## (67.283)
## date_2020_06:Tertiary_industry_ratio 147.558
## (74.940)
## date_2020_07:Tertiary_industry_ratio 133.108
## (76.923)
## date_2020_08:Tertiary_industry_ratio 156.464
## (80.361)
## date_2020_09:Tertiary_industry_ratio 173.517 *
## (82.010)
## date_2020_02:Total_population 0.002
## (0.004)
## date_2020_03:Total_population 0.004
## (0.006)
## date_2020_04:Total_population 0.005
## (0.007)
## date_2020_05:Total_population 0.002
## (0.006)
## date_2020_06:Total_population 0.007
## (0.009)
## date_2020_07:Total_population 0.007
## (0.009)
## date_2020_08:Total_population 0.008
## (0.009)
## date_2020_09:Total_population 0.012
## (0.011)
## date_2020_02:Ratio_of_aged_population -0.077
## (0.271)
## date_2020_03:Ratio_of_aged_population -0.019
## (0.308)
## date_2020_04:Ratio_of_aged_population -0.158
## (0.365)
## date_2020_05:Ratio_of_aged_population -0.246
## (0.365)
## date_2020_06:Ratio_of_aged_population -0.005
## (0.416)
## date_2020_07:Ratio_of_aged_population 0.167
## (0.427)
## date_2020_08:Ratio_of_aged_population 0.407
## (0.450)
## date_2020_09:Ratio_of_aged_population 0.634
## (0.512)
## as.factor(id)1:year_month_id 0.903 ***
## (0.173)
## as.factor(id)2:year_month_id 0.376 ***
## (0.090)
## as.factor(id)3:year_month_id 0.548 **
## (0.175)
## as.factor(id)4:year_month_id 0.140
## (0.231)
## as.factor(id)5:year_month_id 0.056
## (0.128)
## as.factor(id)6:year_month_id 0.370 **
## (0.134)
## as.factor(id)7:year_month_id -0.005
## (0.228)
## as.factor(id)8:year_month_id -0.429 *
## (0.163)
## as.factor(id)9:year_month_id 0.999 ***
## (0.166)
## as.factor(id)10:year_month_id 0.301
## (0.217)
## as.factor(id)11:year_month_id 0.477 *
## (0.203)
## as.factor(id)12:year_month_id 0.056
## (0.234)
## as.factor(id)13:year_month_id -0.205
## (0.215)
## as.factor(id)14:year_month_id -0.054
## (0.202)
## as.factor(id)15:year_month_id -0.026
## (0.168)
## as.factor(id)16:year_month_id 0.380
## (0.269)
## as.factor(id)17:year_month_id 0.645 *
## (0.287)
## as.factor(id)18:year_month_id 0.906 ***
## (0.219)
## as.factor(id)19:year_month_id 0.197
## (0.240)
## as.factor(id)20:year_month_id 0.658 *
## (0.254)
## as.factor(id)21:year_month_id 0.247
## (0.200)
## as.factor(id)22:year_month_id 0.498 *
## (0.220)
## as.factor(id)23:year_month_id 0.546 **
## (0.178)
## as.factor(id)24:year_month_id 0.808 ***
## (0.197)
## as.factor(id)25:year_month_id 0.516 *
## (0.194)
## as.factor(id)26:year_month_id 0.241
## (0.192)
## as.factor(id)27:year_month_id 0.002
## (0.143)
## as.factor(id)28:year_month_id -0.054
## (0.215)
## as.factor(id)29:year_month_id -1.187 ***
## (0.172)
## as.factor(id)30:year_month_id -0.734 ***
## (0.098)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 0.464 ***
## (0.123)
## as.factor(id)33:year_month_id 0.386 *
## (0.169)
## as.factor(id)34:year_month_id 0.891 ***
## (0.191)
## as.factor(id)35:year_month_id 0.283
## (0.226)
## as.factor(id)36:year_month_id 0.766 ***
## (0.144)
## as.factor(id)37:year_month_id 0.199
## (0.207)
## as.factor(id)38:year_month_id 0.528 ***
## (0.094)
## as.factor(id)39:year_month_id 1.000 ***
## (0.161)
## as.factor(id)40:year_month_id 0.767 **
## (0.223)
## as.factor(id)41:year_month_id 0.687 **
## (0.206)
## as.factor(id)42:year_month_id 0.414 *
## (0.184)
## as.factor(id)43:year_month_id 1.617 ***
## (0.062)
## as.factor(id)44:year_month_id 1.298 ***
## (0.081)
## as.factor(id)45:year_month_id 0.905 ***
## (0.100)
## as.factor(id)46:year_month_id 1.492 ***
## (0.125)
## as.factor(id)47:year_month_id 0.943 ***
## (0.232)
## --------------------------------------------------------------------
## R^2 0.974
## Adj. R^2 0.969
## Num. obs. 1551
## RMSE 188.157
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_WLS_trend")
# Event study graph
graph_yoy_hogo_persons_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_WLS_trend")
graph_yoy_hogo_persons_WLS_trend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_persons_WLS_trend_covar <- df_estimates #for robustness check
results_yoy_hogo_persons_WLS_trend_covar <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$job_seeker_total_shock)
#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_persons_WLS_trend")
# Event study graph
graph_yoy_hogo_persons_WLS_trend_covar_onlypost <- event_study_graph(data = df_estimates ,
graph_title = "yoy_hogo_persons_WLS_trend")
ggplotly(graph_yoy_hogo_persons_WLS_trend_covar_onlypost)estimates_yoy_hogo_persons_WLS_trend_covar_onlypost <- df_estimates #for robustness check
results_yoy_hogo_persons_WLS_trend_covar_onlypost <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 62.055
## (31.086)
## treat_var:date_2018_02 63.140 *
## (30.494)
## treat_var:date_2018_03 66.084 *
## (29.531)
## treat_var:date_2018_04 66.068 *
## (27.962)
## treat_var:date_2018_05 66.251 *
## (26.846)
## treat_var:date_2018_06 66.486 *
## (25.107)
## treat_var:date_2018_07 69.373 **
## (25.173)
## treat_var:date_2018_08 74.753 **
## (24.096)
## treat_var:date_2018_09 75.062 **
## (22.376)
## treat_var:date_2018_10 78.937 ***
## (21.144)
## treat_var:date_2018_11 75.311 ***
## (20.089)
## treat_var:date_2018_12 66.752 ***
## (18.654)
## treat_var:date_2019_01 28.110
## (17.586)
## treat_var:date_2019_02 25.999
## (15.013)
## treat_var:date_2019_03 24.380
## (13.990)
## treat_var:date_2019_04 23.093
## (13.079)
## treat_var:date_2019_05 27.807 *
## (12.208)
## treat_var:date_2019_06 28.413 *
## (10.624)
## treat_var:date_2019_07 31.164 **
## (9.668)
## treat_var:date_2019_08 34.371 **
## (10.922)
## treat_var:date_2019_09 34.880 **
## (10.200)
## treat_var:date_2019_10 34.812 **
## (11.207)
## treat_var:date_2019_11 33.947 **
## (11.235)
## treat_var:date_2019_12 34.838 **
## (11.281)
## treat_var:date_2020_02 6.650
## (4.134)
## treat_var:date_2020_03 11.337
## (6.609)
## treat_var:date_2020_04 22.708 *
## (8.699)
## treat_var:date_2020_05 35.287 **
## (12.501)
## treat_var:date_2020_06 48.634 ***
## (13.390)
## treat_var:date_2020_07 51.807 **
## (15.390)
## treat_var:date_2020_08 56.359 **
## (16.618)
## treat_var:date_2020_09 61.148 **
## (18.815)
## ------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 9.368
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_OLS_notrend")
# Event study graph
graph_hogo_households_OLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_OLS_notrend")
graph_hogo_households_OLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_households_OLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 104.306 *
## (45.494)
## treat_var:date_2018_02 103.768 *
## (43.550)
## treat_var:date_2018_03 105.256 *
## (42.466)
## treat_var:date_2018_04 102.360 *
## (39.995)
## treat_var:date_2018_05 104.838 *
## (39.473)
## treat_var:date_2018_06 101.566 **
## (35.980)
## treat_var:date_2018_07 106.778 **
## (35.809)
## treat_var:date_2018_08 111.099 **
## (34.297)
## treat_var:date_2018_09 107.141 **
## (31.367)
## treat_var:date_2018_10 112.307 ***
## (30.982)
## treat_var:date_2018_11 107.499 ***
## (29.406)
## treat_var:date_2018_12 97.339 ***
## (27.592)
## treat_var:date_2019_01 51.619 *
## (23.634)
## treat_var:date_2019_02 46.892 *
## (20.089)
## treat_var:date_2019_03 43.366 *
## (18.884)
## treat_var:date_2019_04 39.031 *
## (17.417)
## treat_var:date_2019_05 42.680 *
## (16.012)
## treat_var:date_2019_06 40.959 **
## (13.638)
## treat_var:date_2019_07 44.634 **
## (13.012)
## treat_var:date_2019_08 45.014 ***
## (12.316)
## treat_var:date_2019_09 44.203 ***
## (11.325)
## treat_var:date_2019_10 44.743 ***
## (11.314)
## treat_var:date_2019_11 42.362 ***
## (10.449)
## treat_var:date_2019_12 42.209 ***
## (10.235)
## treat_var:date_2020_02 4.524
## (3.910)
## treat_var:date_2020_03 8.447
## (4.909)
## treat_var:date_2020_04 18.940 **
## (6.577)
## treat_var:date_2020_05 33.204 ***
## (9.379)
## treat_var:date_2020_06 49.246 ***
## (10.947)
## treat_var:date_2020_07 52.948 ***
## (12.750)
## treat_var:date_2020_08 55.840 ***
## (14.499)
## treat_var:date_2020_09 58.308 **
## (16.951)
## ------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 501.649
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_WLS_notrend")
# Event study graph
graph_hogo_households_WLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_WLS_notrend")
graph_hogo_households_WLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_households_WLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 3.671
## (2.399)
## treat_var:date_2018_03 9.200 *
## (3.940)
## treat_var:date_2018_04 11.770 *
## (5.224)
## treat_var:date_2018_05 14.538 **
## (4.533)
## treat_var:date_2018_06 17.359 **
## (5.993)
## treat_var:date_2018_07 22.831 **
## (6.821)
## treat_var:date_2018_08 30.797 ***
## (8.137)
## treat_var:date_2018_09 33.691 ***
## (8.668)
## treat_var:date_2018_10 40.152 ***
## (8.137)
## treat_var:date_2018_11 39.112 ***
## (9.909)
## treat_var:date_2018_12 33.139 **
## (9.681)
## treat_var:date_2019_01 -2.918
## (6.735)
## treat_var:date_2019_02 -2.443
## (5.438)
## treat_var:date_2019_03 -1.476
## (6.331)
## treat_var:date_2019_04 -0.178
## (7.667)
## treat_var:date_2019_05 7.122
## (8.219)
## treat_var:date_2019_06 10.314
## (7.776)
## treat_var:date_2019_07 15.650 *
## (7.399)
## treat_var:date_2019_08 21.443 *
## (10.240)
## treat_var:date_2019_09 24.538 *
## (9.993)
## treat_var:date_2019_10 27.055 *
## (11.325)
## treat_var:date_2019_11 28.776 *
## (11.523)
## treat_var:date_2019_12 32.252 **
## (11.533)
## treat_var:date_2020_02 9.236 *
## (3.948)
## treat_var:date_2020_03 16.508 *
## (6.279)
## treat_var:date_2020_04 30.465 ***
## (7.832)
## treat_var:date_2020_05 45.630 ***
## (11.540)
## treat_var:date_2020_06 61.562 ***
## (12.497)
## treat_var:date_2020_07 67.321 ***
## (13.955)
## treat_var:date_2020_08 74.459 ***
## (14.957)
## treat_var:date_2020_09 81.834 ***
## (16.745)
## as.factor(id)1:year_month_id -0.781 ***
## (0.064)
## as.factor(id)2:year_month_id -0.166 ***
## (0.020)
## as.factor(id)3:year_month_id -0.224 ***
## (0.035)
## as.factor(id)4:year_month_id 0.652 ***
## (0.023)
## as.factor(id)5:year_month_id -1.085 ***
## (0.034)
## as.factor(id)6:year_month_id -0.054
## (0.036)
## as.factor(id)7:year_month_id
##
## as.factor(id)8:year_month_id -0.101
## (0.066)
## as.factor(id)9:year_month_id -1.284 ***
## (0.066)
## as.factor(id)10:year_month_id -0.877 ***
## (0.063)
## as.factor(id)11:year_month_id -0.578 ***
## (0.050)
## as.factor(id)12:year_month_id -0.035
## (0.082)
## as.factor(id)13:year_month_id -2.698 ***
## (0.069)
## as.factor(id)14:year_month_id -1.090 ***
## (0.061)
## as.factor(id)15:year_month_id -0.654 ***
## (0.049)
## as.factor(id)16:year_month_id -0.614 ***
## (0.074)
## as.factor(id)17:year_month_id -1.691 ***
## (0.043)
## as.factor(id)18:year_month_id -0.573 ***
## (0.036)
## as.factor(id)19:year_month_id -0.729 ***
## (0.050)
## as.factor(id)20:year_month_id -1.004 ***
## (0.038)
## as.factor(id)21:year_month_id -1.405 ***
## (0.102)
## as.factor(id)22:year_month_id -0.441 ***
## (0.087)
## as.factor(id)23:year_month_id -1.592 ***
## (0.096)
## as.factor(id)24:year_month_id -1.306 ***
## (0.070)
## as.factor(id)25:year_month_id -1.484 ***
## (0.077)
## as.factor(id)26:year_month_id -2.305 ***
## (0.088)
## as.factor(id)27:year_month_id -2.484 ***
## (0.078)
## as.factor(id)28:year_month_id -1.672 ***
## (0.072)
## as.factor(id)29:year_month_id -2.440 ***
## (0.066)
## as.factor(id)30:year_month_id -2.344 ***
## (0.086)
## as.factor(id)31:year_month_id -1.749 ***
## (0.035)
## as.factor(id)32:year_month_id -1.777 ***
## (0.068)
## as.factor(id)33:year_month_id -1.668 ***
## (0.026)
## as.factor(id)34:year_month_id -2.214 ***
## (0.076)
## as.factor(id)35:year_month_id -2.478 ***
## (0.036)
## as.factor(id)36:year_month_id -1.509 ***
## (0.042)
## as.factor(id)37:year_month_id -1.346 ***
## (0.034)
## as.factor(id)38:year_month_id -1.350 ***
## (0.040)
## as.factor(id)39:year_month_id -1.718 ***
## (0.003)
## as.factor(id)40:year_month_id -2.339 ***
## (0.063)
## as.factor(id)41:year_month_id -0.500 ***
## (0.022)
## as.factor(id)42:year_month_id -1.259 ***
## (0.020)
## as.factor(id)43:year_month_id -0.868 ***
## (0.042)
## as.factor(id)44:year_month_id -0.257 ***
## (0.062)
## as.factor(id)45:year_month_id -0.553 ***
## (0.079)
## as.factor(id)46:year_month_id -0.977 ***
## (0.001)
## as.factor(id)47:year_month_id 2.063 ***
## (0.068)
## -------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 3.255
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_OLS_trend")
# Event study graph
graph_hogo_households_OLS_trend <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_OLS_trend")
graph_hogo_households_OLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_households_OLS_trend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 3.804
## (2.213)
## treat_var:date_2018_03 9.633 **
## (3.571)
## treat_var:date_2018_04 11.079 *
## (4.319)
## treat_var:date_2018_05 17.899 ***
## (4.882)
## treat_var:date_2018_06 18.969 **
## (6.242)
## treat_var:date_2018_07 28.523 ***
## (7.112)
## treat_var:date_2018_08 37.185 ***
## (8.646)
## treat_var:date_2018_09 37.569 ***
## (9.664)
## treat_var:date_2018_10 47.077 ***
## (9.965)
## treat_var:date_2018_11 46.611 ***
## (12.139)
## treat_var:date_2018_12 40.792 ***
## (11.537)
## treat_var:date_2019_01 -0.612
## (6.139)
## treat_var:date_2019_02 -0.992
## (5.502)
## treat_var:date_2019_03 -0.169
## (6.076)
## treat_var:date_2019_04 -0.156
## (7.277)
## treat_var:date_2019_05 7.841
## (7.703)
## treat_var:date_2019_06 10.469
## (7.801)
## treat_var:date_2019_07 18.493 *
## (7.847)
## treat_var:date_2019_08 23.221 *
## (9.176)
## treat_var:date_2019_09 26.758 **
## (9.288)
## treat_var:date_2019_10 31.647 **
## (9.990)
## treat_var:date_2019_11 33.614 **
## (9.888)
## treat_var:date_2019_12 37.809 ***
## (9.979)
## treat_var:date_2020_02 8.878 **
## (3.139)
## treat_var:date_2020_03 17.156 ***
## (4.771)
## treat_var:date_2020_04 32.005 ***
## (5.777)
## treat_var:date_2020_05 50.623 ***
## (8.639)
## treat_var:date_2020_06 71.019 ***
## (10.220)
## treat_var:date_2020_07 79.077 ***
## (12.062)
## treat_var:date_2020_08 86.324 ***
## (12.480)
## treat_var:date_2020_09 93.146 ***
## (13.500)
## as.factor(id)1:year_month_id 0.997 ***
## (0.003)
## as.factor(id)2:year_month_id 1.629 ***
## (0.037)
## as.factor(id)3:year_month_id 1.596 ***
## (0.079)
## as.factor(id)4:year_month_id 2.448 ***
## (0.035)
## as.factor(id)5:year_month_id 0.710 ***
## (0.026)
## as.factor(id)6:year_month_id 1.736 ***
## (0.024)
## as.factor(id)7:year_month_id 1.807 ***
## (0.052)
## as.factor(id)8:year_month_id 1.678 ***
## (0.002)
## as.factor(id)9:year_month_id 0.493 ***
## (0.001)
## as.factor(id)10:year_month_id 0.902 ***
## (0.003)
## as.factor(id)11:year_month_id 1.206 ***
## (0.014)
## as.factor(id)12:year_month_id 1.735 ***
## (0.011)
## as.factor(id)13:year_month_id -0.922 ***
## (0.001)
## as.factor(id)14:year_month_id 0.690 ***
## (0.005)
## as.factor(id)15:year_month_id 1.132 ***
## (0.014)
## as.factor(id)16:year_month_id 1.161 ***
## (0.005)
## as.factor(id)17:year_month_id 0.096 ***
## (0.019)
## as.factor(id)18:year_month_id 1.217 ***
## (0.024)
## as.factor(id)19:year_month_id 1.056 ***
## (0.014)
## as.factor(id)20:year_month_id 0.785 ***
## (0.023)
## as.factor(id)21:year_month_id 0.358 ***
## (0.026)
## as.factor(id)22:year_month_id 1.328 ***
## (0.015)
## as.factor(id)23:year_month_id 0.173 ***
## (0.022)
## as.factor(id)24:year_month_id 0.469 ***
## (0.002)
## as.factor(id)25:year_month_id 0.289 ***
## (0.007)
## as.factor(id)26:year_month_id -0.536 ***
## (0.016)
## as.factor(id)27:year_month_id -0.711 ***
## (0.008)
## as.factor(id)28:year_month_id 0.103 ***
## (0.004)
## as.factor(id)29:year_month_id -0.662 ***
## (0.001)
## as.factor(id)30:year_month_id -0.574 ***
## (0.014)
## as.factor(id)31:year_month_id 0.043
## (0.025)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id 0.126 ***
## (0.032)
## as.factor(id)34:year_month_id -0.441 ***
## (0.006)
## as.factor(id)35:year_month_id -0.687 ***
## (0.024)
## as.factor(id)36:year_month_id 0.276 ***
## (0.020)
## as.factor(id)37:year_month_id 0.444 ***
## (0.026)
## as.factor(id)38:year_month_id 0.439 ***
## (0.022)
## as.factor(id)39:year_month_id 0.091
## (0.055)
## as.factor(id)40:year_month_id -0.560 ***
## (0.004)
## as.factor(id)41:year_month_id 1.314 ***
## (0.069)
## as.factor(id)42:year_month_id 0.555 ***
## (0.068)
## as.factor(id)43:year_month_id 0.919 ***
## (0.020)
## as.factor(id)44:year_month_id 1.524 ***
## (0.005)
## as.factor(id)45:year_month_id 1.220 ***
## (0.009)
## as.factor(id)46:year_month_id 0.828 ***
## (0.053)
## as.factor(id)47:year_month_id 3.839 ***
## (0.000)
## -------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 167.821
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_WLS_trend")
# Event study graph
graph_hogo_households_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_WLS_trend")
graph_hogo_households_WLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_households_WLS_trend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01 62.055
## (31.801)
## treat_var:date_2018_02 63.140 *
## (31.195)
## treat_var:date_2018_03 66.084 *
## (30.210)
## treat_var:date_2018_04 66.068 *
## (28.605)
## treat_var:date_2018_05 66.251 *
## (27.463)
## treat_var:date_2018_06 66.486 *
## (25.684)
## treat_var:date_2018_07 69.373 **
## (25.752)
## treat_var:date_2018_08 74.753 **
## (24.650)
## treat_var:date_2018_09 75.062 **
## (22.890)
## treat_var:date_2018_10 78.937 ***
## (21.630)
## treat_var:date_2018_11 75.311 ***
## (20.551)
## treat_var:date_2018_12 66.752 **
## (19.083)
## treat_var:date_2019_01 28.110
## (17.990)
## treat_var:date_2019_02 25.999
## (15.358)
## treat_var:date_2019_03 24.380
## (14.312)
## treat_var:date_2019_04 23.093
## (13.380)
## treat_var:date_2019_05 27.807 *
## (12.489)
## treat_var:date_2019_06 28.413 *
## (10.869)
## treat_var:date_2019_07 31.164 **
## (9.890)
## treat_var:date_2019_08 34.371 **
## (11.173)
## treat_var:date_2019_09 34.880 **
## (10.434)
## treat_var:date_2019_10 34.812 **
## (11.465)
## treat_var:date_2019_11 33.947 **
## (11.493)
## treat_var:date_2019_12 34.838 **
## (11.540)
## treat_var:date_2020_02 34.065
## (16.967)
## treat_var:date_2020_03 41.094 *
## (17.557)
## treat_var:date_2020_04 44.743 *
## (18.791)
## treat_var:date_2020_05 45.519 *
## (18.199)
## treat_var:date_2020_06 45.553 *
## (18.770)
## treat_var:date_2020_07 45.342 *
## (19.034)
## treat_var:date_2020_08 42.798 *
## (19.485)
## treat_var:date_2020_09 44.034 *
## (19.892)
## date_2020_02:google_mobility_index_2020may 1.029
## (0.765)
## date_2020_03:google_mobility_index_2020may 1.181
## (0.794)
## date_2020_04:google_mobility_index_2020may 1.139
## (0.838)
## date_2020_05:google_mobility_index_2020may 0.904
## (0.924)
## date_2020_06:google_mobility_index_2020may 0.495
## (0.975)
## date_2020_07:google_mobility_index_2020may 0.481
## (1.094)
## date_2020_08:google_mobility_index_2020may 0.095
## (1.108)
## date_2020_09:google_mobility_index_2020may 0.016
## (1.186)
## date_2020_02:infection_rate_cumulative2020jun -0.090
## (0.703)
## date_2020_03:infection_rate_cumulative2020jun -0.253
## (0.741)
## date_2020_04:infection_rate_cumulative2020jun -0.127
## (0.785)
## date_2020_05:infection_rate_cumulative2020jun -0.496
## (0.844)
## date_2020_06:infection_rate_cumulative2020jun -0.641
## (0.879)
## date_2020_07:infection_rate_cumulative2020jun -0.705
## (0.947)
## date_2020_08:infection_rate_cumulative2020jun -0.903
## (0.961)
## date_2020_09:infection_rate_cumulative2020jun -0.856
## (1.033)
## date_2020_02:death_rate_cumulative2020jun 3.340
## (7.489)
## date_2020_03:death_rate_cumulative2020jun 5.101
## (7.839)
## date_2020_04:death_rate_cumulative2020jun 3.861
## (8.378)
## date_2020_05:death_rate_cumulative2020jun 6.593
## (9.108)
## date_2020_06:death_rate_cumulative2020jun 7.444
## (9.692)
## date_2020_07:death_rate_cumulative2020jun 8.179
## (10.367)
## date_2020_08:death_rate_cumulative2020jun 9.356
## (10.699)
## date_2020_09:death_rate_cumulative2020jun 9.027
## (11.180)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.006 **
## (0.002)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.006 **
## (0.002)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.007 **
## (0.002)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.006 *
## (0.002)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.006 *
## (0.002)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.006 *
## (0.002)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.006 *
## (0.003)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.007 *
## (0.003)
## date_2020_02:Secondary_industry_ratio -174.195 *
## (72.956)
## date_2020_03:Secondary_industry_ratio -206.354 **
## (75.614)
## date_2020_04:Secondary_industry_ratio -190.372 *
## (80.168)
## date_2020_05:Secondary_industry_ratio -177.236 *
## (87.160)
## date_2020_06:Secondary_industry_ratio -151.383
## (90.914)
## date_2020_07:Secondary_industry_ratio -153.562
## (98.218)
## date_2020_08:Secondary_industry_ratio -144.930
## (101.079)
## date_2020_09:Secondary_industry_ratio -144.972
## (103.405)
## date_2020_02:Tertiary_industry_ratio -173.789 *
## (84.514)
## date_2020_03:Tertiary_industry_ratio -197.058 *
## (85.529)
## date_2020_04:Tertiary_industry_ratio -186.066 *
## (90.513)
## date_2020_05:Tertiary_industry_ratio -167.346
## (98.244)
## date_2020_06:Tertiary_industry_ratio -156.547
## (107.556)
## date_2020_07:Tertiary_industry_ratio -171.919
## (113.661)
## date_2020_08:Tertiary_industry_ratio -163.357
## (115.262)
## date_2020_09:Tertiary_industry_ratio -177.515
## (120.007)
## date_2020_02:Total_population 0.008
## (0.014)
## date_2020_03:Total_population 0.009
## (0.015)
## date_2020_04:Total_population 0.011
## (0.016)
## date_2020_05:Total_population 0.006
## (0.018)
## date_2020_06:Total_population 0.008
## (0.018)
## date_2020_07:Total_population 0.006
## (0.019)
## date_2020_08:Total_population 0.008
## (0.020)
## date_2020_09:Total_population 0.010
## (0.021)
## date_2020_02:Ratio_of_aged_population -1.237 *
## (0.470)
## date_2020_03:Ratio_of_aged_population -1.430 **
## (0.496)
## date_2020_04:Ratio_of_aged_population -1.543 **
## (0.524)
## date_2020_05:Ratio_of_aged_population -1.789 **
## (0.588)
## date_2020_06:Ratio_of_aged_population -1.746 **
## (0.595)
## date_2020_07:Ratio_of_aged_population -1.865 **
## (0.654)
## date_2020_08:Ratio_of_aged_population -1.868 **
## (0.664)
## date_2020_09:Ratio_of_aged_population -1.918 **
## (0.691)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 8.526
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_OLS_notrend")
# Event study graph
graph_hogo_households_OLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_OLS_notrend")
graph_hogo_households_OLS_notrend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_households_OLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01 104.336 *
## (46.565)
## treat_var:date_2018_02 103.798 *
## (44.576)
## treat_var:date_2018_03 105.286 *
## (43.468)
## treat_var:date_2018_04 102.390 *
## (40.940)
## treat_var:date_2018_05 104.868 *
## (40.406)
## treat_var:date_2018_06 101.597 **
## (36.832)
## treat_var:date_2018_07 106.808 **
## (36.658)
## treat_var:date_2018_08 111.129 **
## (35.110)
## treat_var:date_2018_09 107.172 **
## (32.113)
## treat_var:date_2018_10 112.337 ***
## (31.721)
## treat_var:date_2018_11 107.529 ***
## (30.112)
## treat_var:date_2018_12 97.369 **
## (28.257)
## treat_var:date_2019_01 51.634 *
## (24.191)
## treat_var:date_2019_02 46.907 *
## (20.563)
## treat_var:date_2019_03 43.381 *
## (19.331)
## treat_var:date_2019_04 39.046 *
## (17.831)
## treat_var:date_2019_05 42.695 *
## (16.393)
## treat_var:date_2019_06 40.974 **
## (13.964)
## treat_var:date_2019_07 44.649 **
## (13.323)
## treat_var:date_2019_08 45.029 ***
## (12.608)
## treat_var:date_2019_09 44.218 ***
## (11.594)
## treat_var:date_2019_10 44.758 ***
## (11.583)
## treat_var:date_2019_11 42.377 ***
## (10.695)
## treat_var:date_2019_12 42.224 ***
## (10.476)
## treat_var:date_2020_02 43.865
## (26.362)
## treat_var:date_2020_03 52.393
## (28.002)
## treat_var:date_2020_04 53.968
## (29.022)
## treat_var:date_2020_05 51.023
## (28.703)
## treat_var:date_2020_06 48.011
## (28.733)
## treat_var:date_2020_07 47.398
## (30.576)
## treat_var:date_2020_08 41.088
## (31.169)
## treat_var:date_2020_09 39.735
## (32.937)
## date_2020_02:google_mobility_index_2020may -0.369
## (1.080)
## date_2020_03:google_mobility_index_2020may -0.307
## (1.122)
## date_2020_04:google_mobility_index_2020may -0.419
## (1.209)
## date_2020_05:google_mobility_index_2020may -0.917
## (1.341)
## date_2020_06:google_mobility_index_2020may -1.403
## (1.392)
## date_2020_07:google_mobility_index_2020may -1.405
## (1.478)
## date_2020_08:google_mobility_index_2020may -1.843
## (1.557)
## date_2020_09:google_mobility_index_2020may -2.096
## (1.659)
## date_2020_02:infection_rate_cumulative2020jun -1.387
## (0.743)
## date_2020_03:infection_rate_cumulative2020jun -1.645 *
## (0.783)
## date_2020_04:infection_rate_cumulative2020jun -1.567
## (0.830)
## date_2020_05:infection_rate_cumulative2020jun -1.831 *
## (0.844)
## date_2020_06:infection_rate_cumulative2020jun -2.017 *
## (0.864)
## date_2020_07:infection_rate_cumulative2020jun -2.178 *
## (0.901)
## date_2020_08:infection_rate_cumulative2020jun -2.313 *
## (0.915)
## date_2020_09:infection_rate_cumulative2020jun -2.519 *
## (1.006)
## date_2020_02:death_rate_cumulative2020jun 13.957
## (7.965)
## date_2020_03:death_rate_cumulative2020jun 16.796
## (8.352)
## date_2020_04:death_rate_cumulative2020jun 16.031
## (8.922)
## date_2020_05:death_rate_cumulative2020jun 16.679
## (9.203)
## date_2020_06:death_rate_cumulative2020jun 17.105
## (9.578)
## date_2020_07:death_rate_cumulative2020jun 18.549
## (9.958)
## date_2020_08:death_rate_cumulative2020jun 18.550
## (10.320)
## date_2020_09:death_rate_cumulative2020jun 20.356
## (11.153)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.004 **
## (0.002)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.004 **
## (0.002)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.005 **
## (0.002)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.004 *
## (0.002)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.004 *
## (0.002)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.004
## (0.002)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.005 *
## (0.002)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.005 *
## (0.002)
## date_2020_02:Secondary_industry_ratio -174.163 *
## (77.677)
## date_2020_03:Secondary_industry_ratio -205.927 *
## (80.258)
## date_2020_04:Secondary_industry_ratio -194.906 *
## (86.181)
## date_2020_05:Secondary_industry_ratio -182.158
## (92.527)
## date_2020_06:Secondary_industry_ratio -149.137
## (95.300)
## date_2020_07:Secondary_industry_ratio -154.687
## (102.540)
## date_2020_08:Secondary_industry_ratio -140.692
## (106.861)
## date_2020_09:Secondary_industry_ratio -140.892
## (110.579)
## date_2020_02:Tertiary_industry_ratio -132.093
## (95.115)
## date_2020_03:Tertiary_industry_ratio -147.717
## (97.256)
## date_2020_04:Tertiary_industry_ratio -139.630
## (103.999)
## date_2020_05:Tertiary_industry_ratio -122.356
## (110.583)
## date_2020_06:Tertiary_industry_ratio -100.072
## (115.062)
## date_2020_07:Tertiary_industry_ratio -117.875
## (121.539)
## date_2020_08:Tertiary_industry_ratio -103.462
## (125.613)
## date_2020_09:Tertiary_industry_ratio -106.850
## (134.216)
## date_2020_02:Total_population 0.006
## (0.013)
## date_2020_03:Total_population 0.006
## (0.013)
## date_2020_04:Total_population 0.008
## (0.015)
## date_2020_05:Total_population 0.003
## (0.016)
## date_2020_06:Total_population 0.006
## (0.016)
## date_2020_07:Total_population 0.006
## (0.018)
## date_2020_08:Total_population 0.008
## (0.019)
## date_2020_09:Total_population 0.010
## (0.020)
## date_2020_02:Ratio_of_aged_population -0.673
## (0.552)
## date_2020_03:Ratio_of_aged_population -0.753
## (0.589)
## date_2020_04:Ratio_of_aged_population -0.824
## (0.633)
## date_2020_05:Ratio_of_aged_population -1.003
## (0.690)
## date_2020_06:Ratio_of_aged_population -0.968
## (0.699)
## date_2020_07:Ratio_of_aged_population -1.096
## (0.745)
## date_2020_08:Ratio_of_aged_population -1.105
## (0.774)
## date_2020_09:Ratio_of_aged_population -1.097
## (0.801)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 433.892
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_WLS_notrend")
# Event study graph
graph_hogo_households_WLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_WLS_notrend")
graph_hogo_households_WLS_notrend_covar## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_hogo_households_WLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 3.671
## (2.456)
## treat_var:date_2018_03 9.200 *
## (4.033)
## treat_var:date_2018_04 11.770 *
## (5.348)
## treat_var:date_2018_05 14.538 **
## (4.641)
## treat_var:date_2018_06 17.359 **
## (6.136)
## treat_var:date_2018_07 22.831 **
## (6.983)
## treat_var:date_2018_08 30.797 ***
## (8.331)
## treat_var:date_2018_09 33.691 ***
## (8.874)
## treat_var:date_2018_10 40.152 ***
## (8.331)
## treat_var:date_2018_11 39.112 ***
## (10.144)
## treat_var:date_2018_12 33.139 **
## (9.911)
## treat_var:date_2019_01 -2.918
## (6.895)
## treat_var:date_2019_02 -2.443
## (5.567)
## treat_var:date_2019_03 -1.476
## (6.481)
## treat_var:date_2019_04 -0.178
## (7.849)
## treat_var:date_2019_05 7.122
## (8.415)
## treat_var:date_2019_06 10.314
## (7.960)
## treat_var:date_2019_07 15.650 *
## (7.574)
## treat_var:date_2019_08 21.443 *
## (10.484)
## treat_var:date_2019_09 24.538 *
## (10.230)
## treat_var:date_2019_10 27.055 *
## (11.594)
## treat_var:date_2019_11 28.776 *
## (11.796)
## treat_var:date_2019_12 32.252 **
## (11.808)
## treat_var:date_2020_02 27.487 *
## (11.824)
## treat_var:date_2020_03 36.396 **
## (12.584)
## treat_var:date_2020_04 41.926 **
## (14.227)
## treat_var:date_2020_05 44.583 **
## (14.639)
## treat_var:date_2020_06 46.498 **
## (14.210)
## treat_var:date_2020_07 48.167 **
## (16.159)
## treat_var:date_2020_08 47.505 **
## (16.634)
## treat_var:date_2020_09 50.621 *
## (20.909)
## date_2020_02:google_mobility_index_2020may 0.446
## (0.467)
## date_2020_03:google_mobility_index_2020may 0.554
## (0.465)
## date_2020_04:google_mobility_index_2020may 0.467
## (0.543)
## date_2020_05:google_mobility_index_2020may 0.187
## (0.495)
## date_2020_06:google_mobility_index_2020may -0.267
## (0.542)
## date_2020_07:google_mobility_index_2020may -0.326
## (0.614)
## date_2020_08:google_mobility_index_2020may -0.756
## (0.621)
## date_2020_09:google_mobility_index_2020may -0.880
## (0.700)
## date_2020_02:infection_rate_cumulative2020jun -0.021
## (0.263)
## date_2020_03:infection_rate_cumulative2020jun -0.179
## (0.270)
## date_2020_04:infection_rate_cumulative2020jun -0.048
## (0.298)
## date_2020_05:infection_rate_cumulative2020jun -0.411
## (0.342)
## date_2020_06:infection_rate_cumulative2020jun -0.550
## (0.396)
## date_2020_07:infection_rate_cumulative2020jun -0.610
## (0.459)
## date_2020_08:infection_rate_cumulative2020jun -0.802
## (0.501)
## date_2020_09:infection_rate_cumulative2020jun -0.750
## (0.533)
## date_2020_02:death_rate_cumulative2020jun 1.578
## (2.653)
## date_2020_03:death_rate_cumulative2020jun 3.204
## (2.881)
## date_2020_04:death_rate_cumulative2020jun 1.828
## (3.209)
## date_2020_05:death_rate_cumulative2020jun 4.425
## (3.537)
## date_2020_06:death_rate_cumulative2020jun 5.140
## (4.035)
## date_2020_07:death_rate_cumulative2020jun 5.739
## (4.527)
## date_2020_08:death_rate_cumulative2020jun 6.781
## (5.086)
## date_2020_09:death_rate_cumulative2020jun 6.317
## (5.413)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_02:Secondary_industry_ratio -18.254
## (32.410)
## date_2020_03:Secondary_industry_ratio -38.417
## (33.906)
## date_2020_04:Secondary_industry_ratio -10.440
## (38.708)
## date_2020_05:Secondary_industry_ratio 14.692
## (34.337)
## date_2020_06:Secondary_industry_ratio 52.540
## (40.844)
## date_2020_07:Secondary_industry_ratio 62.356
## (42.724)
## date_2020_08:Secondary_industry_ratio 82.984
## (43.855)
## date_2020_09:Secondary_industry_ratio 94.937
## (51.132)
## date_2020_02:Tertiary_industry_ratio -8.730
## (32.195)
## date_2020_03:Tertiary_industry_ratio -19.303
## (35.923)
## date_2020_04:Tertiary_industry_ratio 4.387
## (38.991)
## date_2020_05:Tertiary_industry_ratio 35.804
## (38.802)
## date_2020_06:Tertiary_industry_ratio 59.299
## (46.709)
## date_2020_07:Tertiary_industry_ratio 56.624
## (48.640)
## date_2020_08:Tertiary_industry_ratio 77.882
## (50.393)
## date_2020_09:Tertiary_industry_ratio 76.421
## (54.280)
## date_2020_02:Total_population 0.003
## (0.005)
## date_2020_03:Total_population 0.003
## (0.006)
## date_2020_04:Total_population 0.005
## (0.007)
## date_2020_05:Total_population -0.001
## (0.006)
## date_2020_06:Total_population 0.001
## (0.006)
## date_2020_07:Total_population -0.001
## (0.006)
## date_2020_08:Total_population -0.000
## (0.007)
## date_2020_09:Total_population 0.002
## (0.008)
## date_2020_02:Ratio_of_aged_population 0.142
## (0.214)
## date_2020_03:Ratio_of_aged_population 0.056
## (0.210)
## date_2020_04:Ratio_of_aged_population 0.049
## (0.246)
## date_2020_05:Ratio_of_aged_population -0.091
## (0.232)
## date_2020_06:Ratio_of_aged_population 0.058
## (0.275)
## date_2020_07:Ratio_of_aged_population 0.045
## (0.291)
## date_2020_08:Ratio_of_aged_population 0.149
## (0.307)
## date_2020_09:Ratio_of_aged_population 0.204
## (0.344)
## as.factor(id)1:year_month_id
##
## as.factor(id)2:year_month_id 0.616 ***
## (0.133)
## as.factor(id)3:year_month_id 0.455 ***
## (0.128)
## as.factor(id)4:year_month_id 1.323 ***
## (0.130)
## as.factor(id)5:year_month_id -0.356 **
## (0.132)
## as.factor(id)6:year_month_id 0.756 ***
## (0.110)
## as.factor(id)7:year_month_id 0.735 ***
## (0.117)
## as.factor(id)8:year_month_id 0.628 ***
## (0.105)
## as.factor(id)9:year_month_id -0.526 ***
## (0.123)
## as.factor(id)10:year_month_id -0.247
## (0.145)
## as.factor(id)11:year_month_id 0.093
## (0.083)
## as.factor(id)12:year_month_id 0.694 ***
## (0.096)
## as.factor(id)13:year_month_id -2.039 ***
## (0.137)
## as.factor(id)14:year_month_id -0.586 ***
## (0.154)
## as.factor(id)15:year_month_id 0.068
## (0.105)
## as.factor(id)16:year_month_id 0.030
## (0.128)
## as.factor(id)17:year_month_id -1.027 ***
## (0.126)
## as.factor(id)18:year_month_id 0.145
## (0.119)
## as.factor(id)19:year_month_id 0.050
## (0.137)
## as.factor(id)20:year_month_id -0.270
## (0.139)
## as.factor(id)21:year_month_id -0.676 ***
## (0.131)
## as.factor(id)22:year_month_id 0.227
## (0.141)
## as.factor(id)23:year_month_id -0.946 ***
## (0.124)
## as.factor(id)24:year_month_id -0.622 ***
## (0.135)
## as.factor(id)25:year_month_id -0.718 ***
## (0.148)
## as.factor(id)26:year_month_id -1.547 ***
## (0.136)
## as.factor(id)27:year_month_id -1.866 ***
## (0.142)
## as.factor(id)28:year_month_id -1.021 ***
## (0.097)
## as.factor(id)29:year_month_id -1.755 ***
## (0.141)
## as.factor(id)30:year_month_id -1.567 ***
## (0.119)
## as.factor(id)31:year_month_id -1.027 ***
## (0.128)
## as.factor(id)32:year_month_id -1.027 ***
## (0.120)
## as.factor(id)33:year_month_id -0.993 ***
## (0.121)
## as.factor(id)34:year_month_id -1.512 ***
## (0.119)
## as.factor(id)35:year_month_id -1.824 ***
## (0.146)
## as.factor(id)36:year_month_id -0.833 ***
## (0.145)
## as.factor(id)37:year_month_id -0.673 ***
## (0.142)
## as.factor(id)38:year_month_id -0.607 ***
## (0.104)
## as.factor(id)39:year_month_id -0.938 ***
## (0.117)
## as.factor(id)40:year_month_id -1.588 ***
## (0.106)
## as.factor(id)41:year_month_id 0.249
## (0.128)
## as.factor(id)42:year_month_id -0.624 ***
## (0.140)
## as.factor(id)43:year_month_id -0.148
## (0.107)
## as.factor(id)44:year_month_id 0.518 ***
## (0.104)
## as.factor(id)45:year_month_id 0.227
## (0.133)
## as.factor(id)46:year_month_id -0.300 *
## (0.116)
## as.factor(id)47:year_month_id 2.840 ***
## (0.207)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 3.056
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_OLS_trend")
# Event study graph
graph_hogo_households_OLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_OLS_trend")
estimates_hogo_households_OLS_trend_covar <- df_estimates# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 3.804
## (2.266)
## treat_var:date_2018_03 9.634 *
## (3.656)
## treat_var:date_2018_04 11.080 *
## (4.423)
## treat_var:date_2018_05 17.901 ***
## (4.999)
## treat_var:date_2018_06 18.971 **
## (6.391)
## treat_var:date_2018_07 28.525 ***
## (7.281)
## treat_var:date_2018_08 37.188 ***
## (8.851)
## treat_var:date_2018_09 37.572 ***
## (9.894)
## treat_var:date_2018_10 47.080 ***
## (10.202)
## treat_var:date_2018_11 46.614 ***
## (12.428)
## treat_var:date_2018_12 40.796 **
## (11.812)
## treat_var:date_2019_01 -0.609
## (6.286)
## treat_var:date_2019_02 -0.988
## (5.632)
## treat_var:date_2019_03 -0.166
## (6.221)
## treat_var:date_2019_04 -0.152
## (7.451)
## treat_var:date_2019_05 7.845
## (7.887)
## treat_var:date_2019_06 10.473
## (7.987)
## treat_var:date_2019_07 18.496 *
## (8.034)
## treat_var:date_2019_08 23.224 *
## (9.394)
## treat_var:date_2019_09 26.761 **
## (9.508)
## treat_var:date_2019_10 31.650 **
## (10.228)
## treat_var:date_2019_11 33.617 **
## (10.122)
## treat_var:date_2019_12 37.813 ***
## (10.216)
## treat_var:date_2020_02 41.230 **
## (12.117)
## treat_var:date_2020_03 53.573 ***
## (13.409)
## treat_var:date_2020_04 58.963 ***
## (14.585)
## treat_var:date_2020_05 59.833 ***
## (13.677)
## treat_var:date_2020_06 60.636 ***
## (13.640)
## treat_var:date_2020_07 63.838 ***
## (14.873)
## treat_var:date_2020_08 61.344 ***
## (14.987)
## treat_var:date_2020_09 63.806 ***
## (18.092)
## date_2020_02:google_mobility_index_2020may 0.807 *
## (0.393)
## date_2020_03:google_mobility_index_2020may 0.959 *
## (0.466)
## date_2020_04:google_mobility_index_2020may 0.939
## (0.521)
## date_2020_05:google_mobility_index_2020may 0.532
## (0.460)
## date_2020_06:google_mobility_index_2020may 0.137
## (0.511)
## date_2020_07:google_mobility_index_2020may 0.225
## (0.550)
## date_2020_08:google_mobility_index_2020may -0.121
## (0.552)
## date_2020_09:google_mobility_index_2020may -0.284
## (0.587)
## date_2020_02:infection_rate_cumulative2020jun -0.195
## (0.214)
## date_2020_03:infection_rate_cumulative2020jun -0.361
## (0.260)
## date_2020_04:infection_rate_cumulative2020jun -0.191
## (0.274)
## date_2020_05:infection_rate_cumulative2020jun -0.363
## (0.280)
## date_2020_06:infection_rate_cumulative2020jun -0.457
## (0.334)
## date_2020_07:infection_rate_cumulative2020jun -0.526
## (0.360)
## date_2020_08:infection_rate_cumulative2020jun -0.569
## (0.395)
## date_2020_09:infection_rate_cumulative2020jun -0.683
## (0.421)
## date_2020_02:death_rate_cumulative2020jun 4.094
## (2.775)
## date_2020_03:death_rate_cumulative2020jun 6.172
## (3.526)
## date_2020_04:death_rate_cumulative2020jun 4.644
## (3.716)
## date_2020_05:death_rate_cumulative2020jun 4.531
## (3.385)
## date_2020_06:death_rate_cumulative2020jun 4.196
## (3.773)
## date_2020_07:death_rate_cumulative2020jun 4.878
## (4.081)
## date_2020_08:death_rate_cumulative2020jun 4.118
## (4.425)
## date_2020_09:death_rate_cumulative2020jun 5.161
## (4.691)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_02:Secondary_industry_ratio -10.105
## (32.009)
## date_2020_03:Secondary_industry_ratio -29.242
## (36.332)
## date_2020_04:Secondary_industry_ratio -5.594
## (39.323)
## date_2020_05:Secondary_industry_ratio 19.781
## (35.327)
## date_2020_06:Secondary_industry_ratio 65.430
## (41.518)
## date_2020_07:Secondary_industry_ratio 72.507
## (41.686)
## date_2020_08:Secondary_industry_ratio 99.130 *
## (44.155)
## date_2020_09:Secondary_industry_ratio 111.557 *
## (50.706)
## date_2020_02:Tertiary_industry_ratio 20.745
## (30.781)
## date_2020_03:Tertiary_industry_ratio 16.852
## (38.625)
## date_2020_04:Tertiary_industry_ratio 36.672
## (40.168)
## date_2020_05:Tertiary_industry_ratio 65.676
## (40.967)
## date_2020_06:Tertiary_industry_ratio 99.692 *
## (49.075)
## date_2020_07:Tertiary_industry_ratio 93.621
## (50.410)
## date_2020_08:Tertiary_industry_ratio 119.765 *
## (53.545)
## date_2020_09:Tertiary_industry_ratio 128.110 *
## (56.902)
## date_2020_02:Total_population 0.001
## (0.005)
## date_2020_03:Total_population 0.000
## (0.006)
## date_2020_04:Total_population 0.002
## (0.006)
## date_2020_05:Total_population -0.003
## (0.005)
## date_2020_06:Total_population -0.000
## (0.006)
## date_2020_07:Total_population -0.001
## (0.005)
## date_2020_08:Total_population 0.001
## (0.005)
## date_2020_09:Total_population 0.003
## (0.006)
## date_2020_02:Ratio_of_aged_population 0.104
## (0.181)
## date_2020_03:Ratio_of_aged_population 0.083
## (0.197)
## date_2020_04:Ratio_of_aged_population 0.072
## (0.227)
## date_2020_05:Ratio_of_aged_population -0.047
## (0.210)
## date_2020_06:Ratio_of_aged_population 0.047
## (0.253)
## date_2020_07:Ratio_of_aged_population -0.022
## (0.248)
## date_2020_08:Ratio_of_aged_population 0.030
## (0.260)
## date_2020_09:Ratio_of_aged_population 0.097
## (0.291)
## as.factor(id)1:year_month_id 1.067 ***
## (0.111)
## as.factor(id)2:year_month_id 1.643 ***
## (0.045)
## as.factor(id)3:year_month_id 1.569 ***
## (0.084)
## as.factor(id)4:year_month_id 2.405 ***
## (0.113)
## as.factor(id)5:year_month_id 0.650 ***
## (0.087)
## as.factor(id)6:year_month_id 1.852 ***
## (0.081)
## as.factor(id)7:year_month_id 1.903 ***
## (0.110)
## as.factor(id)8:year_month_id 1.753 ***
## (0.085)
## as.factor(id)9:year_month_id 0.606 ***
## (0.090)
## as.factor(id)10:year_month_id 0.874 ***
## (0.113)
## as.factor(id)11:year_month_id 1.206 ***
## (0.094)
## as.factor(id)12:year_month_id 1.776 ***
## (0.124)
## as.factor(id)13:year_month_id -0.884 ***
## (0.097)
## as.factor(id)14:year_month_id 0.490 ***
## (0.091)
## as.factor(id)15:year_month_id 1.126 ***
## (0.089)
## as.factor(id)16:year_month_id 1.094 ***
## (0.136)
## as.factor(id)17:year_month_id 0.095
## (0.140)
## as.factor(id)18:year_month_id 1.187 ***
## (0.115)
## as.factor(id)19:year_month_id 1.202 ***
## (0.118)
## as.factor(id)20:year_month_id 0.922 ***
## (0.134)
## as.factor(id)21:year_month_id 0.394 ***
## (0.102)
## as.factor(id)22:year_month_id 1.337 ***
## (0.113)
## as.factor(id)23:year_month_id 0.151
## (0.095)
## as.factor(id)24:year_month_id 0.481 ***
## (0.096)
## as.factor(id)25:year_month_id 0.375 **
## (0.113)
## as.factor(id)26:year_month_id -0.434 ***
## (0.095)
## as.factor(id)27:year_month_id -0.805 ***
## (0.072)
## as.factor(id)28:year_month_id 0.031
## (0.103)
## as.factor(id)29:year_month_id -0.765 ***
## (0.100)
## as.factor(id)30:year_month_id -0.555 ***
## (0.049)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id -0.013
## (0.070)
## as.factor(id)33:year_month_id 0.120
## (0.078)
## as.factor(id)34:year_month_id -0.458 ***
## (0.097)
## as.factor(id)35:year_month_id -0.777 ***
## (0.114)
## as.factor(id)36:year_month_id 0.247 **
## (0.077)
## as.factor(id)37:year_month_id 0.423 ***
## (0.096)
## as.factor(id)38:year_month_id 0.471 ***
## (0.051)
## as.factor(id)39:year_month_id 0.175
## (0.089)
## as.factor(id)40:year_month_id -0.529 ***
## (0.123)
## as.factor(id)41:year_month_id 1.397 ***
## (0.099)
## as.factor(id)42:year_month_id 0.459 ***
## (0.087)
## as.factor(id)43:year_month_id 0.908 ***
## (0.034)
## as.factor(id)44:year_month_id 1.548 ***
## (0.048)
## as.factor(id)45:year_month_id 1.219 ***
## (0.048)
## as.factor(id)46:year_month_id 0.759 ***
## (0.061)
## as.factor(id)47:year_month_id 3.957 ***
## (0.149)
## --------------------------------------------------------------------
## R^2 1.000
## Adj. R^2 1.000
## Num. obs. 1551
## RMSE 148.863
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "hogo_households_WLS_trend")
# Event study graph
graph_hogo_households_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "hogo_households_WLS_trend")
estimates_hogo_households_WLS_trend_covar <- df_estimates# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 15.161
## (16.012)
## treat_var:date_2018_02 12.677
## (15.976)
## treat_var:date_2018_03 15.377
## (15.515)
## treat_var:date_2018_04 11.404
## (15.323)
## treat_var:date_2018_05 8.129
## (15.318)
## treat_var:date_2018_06 4.589
## (14.977)
## treat_var:date_2018_07 4.466
## (15.325)
## treat_var:date_2018_08 19.620
## (22.852)
## treat_var:date_2018_09 8.684
## (15.194)
## treat_var:date_2018_10 12.498
## (14.461)
## treat_var:date_2018_11 8.115
## (13.760)
## treat_var:date_2018_12 0.855
## (13.062)
## treat_var:date_2019_01 -5.835
## (13.257)
## treat_var:date_2019_02 -9.032
## (10.574)
## treat_var:date_2019_03 -13.594
## (9.800)
## treat_var:date_2019_04 -14.865
## (11.237)
## treat_var:date_2019_05 -10.333
## (10.251)
## treat_var:date_2019_06 -9.963
## (9.098)
## treat_var:date_2019_07 -10.099
## (8.210)
## treat_var:date_2019_08 -12.271
## (7.019)
## treat_var:date_2019_09 -12.071
## (6.158)
## treat_var:date_2019_10 -16.015 **
## (5.957)
## treat_var:date_2019_11 -13.254 **
## (4.688)
## treat_var:date_2019_12 -3.804
## (3.665)
## treat_var:date_2020_02 8.762 **
## (3.159)
## treat_var:date_2020_03 15.067 **
## (4.825)
## treat_var:date_2020_04 27.725 ***
## (5.760)
## treat_var:date_2020_05 35.590 ***
## (6.990)
## treat_var:date_2020_06 48.331 ***
## (7.741)
## treat_var:date_2020_07 48.753 ***
## (8.881)
## treat_var:date_2020_08 50.098 ***
## (9.306)
## treat_var:date_2020_09 54.378 ***
## (11.046)
## ------------------------------------
## R^2 0.814
## Adj. R^2 0.800
## Num. obs. 1551
## RMSE 5.306
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_OLS_notrend")
# Event study graph
graph_yoy_hogo_households_OLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_OLS_notrend")
graph_yoy_hogo_households_OLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_households_OLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ====================================
## Model 1
## ------------------------------------
## treat_var:date_2018_01 22.596
## (16.261)
## treat_var:date_2018_02 20.833
## (15.868)
## treat_var:date_2018_03 22.007
## (14.471)
## treat_var:date_2018_04 17.453
## (13.584)
## treat_var:date_2018_05 16.395
## (14.720)
## treat_var:date_2018_06 8.954
## (14.447)
## treat_var:date_2018_07 10.601
## (14.765)
## treat_var:date_2018_08 19.275
## (15.692)
## treat_var:date_2018_09 11.682
## (13.774)
## treat_var:date_2018_10 14.653
## (13.414)
## treat_var:date_2018_11 10.071
## (13.560)
## treat_var:date_2018_12 4.127
## (11.868)
## treat_var:date_2019_01 -1.181
## (12.089)
## treat_var:date_2019_02 -5.369
## (10.009)
## treat_var:date_2019_03 -10.374
## (9.465)
## treat_var:date_2019_04 -11.793
## (10.577)
## treat_var:date_2019_05 -10.636
## (10.136)
## treat_var:date_2019_06 -9.079
## (9.137)
## treat_var:date_2019_07 -10.617
## (8.154)
## treat_var:date_2019_08 -14.554 *
## (7.139)
## treat_var:date_2019_09 -11.388
## (6.604)
## treat_var:date_2019_10 -16.007 **
## (5.897)
## treat_var:date_2019_11 -13.575 **
## (4.660)
## treat_var:date_2019_12 -3.566
## (3.582)
## treat_var:date_2020_02 9.266 **
## (3.384)
## treat_var:date_2020_03 16.710 **
## (5.278)
## treat_var:date_2020_04 31.547 ***
## (5.955)
## treat_var:date_2020_05 42.164 ***
## (7.645)
## treat_var:date_2020_06 59.936 ***
## (9.134)
## treat_var:date_2020_07 59.969 ***
## (9.335)
## treat_var:date_2020_08 62.500 ***
## (9.762)
## treat_var:date_2020_09 65.789 ***
## (10.092)
## ------------------------------------
## R^2 0.889
## Adj. R^2 0.881
## Num. obs. 1551
## RMSE 217.565
## N Clusters 47
## ====================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_WLS_notrend")
# Event study graph
graph_yoy_hogo_households_WLS_notrend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_WLS_notrend")
graph_yoy_hogo_households_WLS_notrend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_households_WLS_notrend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -1.852
## (4.561)
## treat_var:date_2018_03 1.480
## (6.434)
## treat_var:date_2018_04 -1.862
## (6.811)
## treat_var:date_2018_05 -4.505
## (7.592)
## treat_var:date_2018_06 -7.413
## (7.458)
## treat_var:date_2018_07 -6.905
## (8.792)
## treat_var:date_2018_08 8.881
## (15.647)
## treat_var:date_2018_09 -1.423
## (10.889)
## treat_var:date_2018_10 3.022
## (10.902)
## treat_var:date_2018_11 -0.728
## (10.707)
## treat_var:date_2018_12 -7.357
## (10.402)
## treat_var:date_2019_01 -13.415
## (11.477)
## treat_var:date_2019_02 -15.980
## (9.270)
## treat_var:date_2019_03 -19.911 *
## (8.648)
## treat_var:date_2019_04 -20.551
## (10.606)
## treat_var:date_2019_05 -15.387
## (9.321)
## treat_var:date_2019_06 -14.385
## (8.486)
## treat_var:date_2019_07 -13.889
## (7.973)
## treat_var:date_2019_08 -15.429 *
## (6.659)
## treat_var:date_2019_09 -14.598 *
## (5.919)
## treat_var:date_2019_10 -17.910 **
## (5.665)
## treat_var:date_2019_11 -14.517 **
## (4.567)
## treat_var:date_2019_12 -4.436
## (3.746)
## treat_var:date_2020_02 9.393 *
## (3.594)
## treat_var:date_2020_03 16.330 **
## (5.509)
## treat_var:date_2020_04 29.620 ***
## (6.628)
## treat_var:date_2020_05 38.117 ***
## (8.255)
## treat_var:date_2020_06 51.489 ***
## (9.370)
## treat_var:date_2020_07 52.544 ***
## (11.235)
## treat_var:date_2020_08 54.520 ***
## (11.892)
## treat_var:date_2020_09 59.432 ***
## (14.600)
## as.factor(id)1:year_month_id 0.374 ***
## (0.076)
## as.factor(id)2:year_month_id 0.123 ***
## (0.023)
## as.factor(id)3:year_month_id 0.464 ***
## (0.041)
## as.factor(id)4:year_month_id 0.260 ***
## (0.027)
## as.factor(id)5:year_month_id 0.142 **
## (0.041)
## as.factor(id)6:year_month_id 0.173 ***
## (0.043)
## as.factor(id)7:year_month_id
##
## as.factor(id)8:year_month_id -0.350 ***
## (0.077)
## as.factor(id)9:year_month_id 0.545 ***
## (0.078)
## as.factor(id)10:year_month_id 0.076
## (0.075)
## as.factor(id)11:year_month_id 0.411 ***
## (0.059)
## as.factor(id)12:year_month_id 0.083
## (0.097)
## as.factor(id)13:year_month_id 0.094
## (0.081)
## as.factor(id)14:year_month_id 0.492 ***
## (0.072)
## as.factor(id)15:year_month_id 0.035
## (0.058)
## as.factor(id)16:year_month_id 0.430 ***
## (0.087)
## as.factor(id)17:year_month_id 0.737 ***
## (0.051)
## as.factor(id)18:year_month_id 0.709 ***
## (0.043)
## as.factor(id)19:year_month_id 0.028
## (0.059)
## as.factor(id)20:year_month_id 0.425 ***
## (0.044)
## as.factor(id)21:year_month_id 0.103
## (0.120)
## as.factor(id)22:year_month_id 0.451 ***
## (0.103)
## as.factor(id)23:year_month_id 0.441 ***
## (0.114)
## as.factor(id)24:year_month_id 0.643 ***
## (0.083)
## as.factor(id)25:year_month_id 0.418 ***
## (0.091)
## as.factor(id)26:year_month_id 0.032
## (0.104)
## as.factor(id)27:year_month_id 0.247 *
## (0.092)
## as.factor(id)28:year_month_id 0.116
## (0.085)
## as.factor(id)29:year_month_id -0.563 ***
## (0.078)
## as.factor(id)30:year_month_id -0.840 ***
## (0.101)
## as.factor(id)31:year_month_id -0.177 ***
## (0.042)
## as.factor(id)32:year_month_id 0.390 ***
## (0.080)
## as.factor(id)33:year_month_id 0.302 ***
## (0.030)
## as.factor(id)34:year_month_id 0.630 ***
## (0.090)
## as.factor(id)35:year_month_id 0.148 **
## (0.043)
## as.factor(id)36:year_month_id 0.719 ***
## (0.050)
## as.factor(id)37:year_month_id 0.148 ***
## (0.041)
## as.factor(id)38:year_month_id 0.291 ***
## (0.047)
## as.factor(id)39:year_month_id 0.101 ***
## (0.004)
## as.factor(id)40:year_month_id 0.599 ***
## (0.074)
## as.factor(id)41:year_month_id 0.602 ***
## (0.025)
## as.factor(id)42:year_month_id 0.137 ***
## (0.024)
## as.factor(id)43:year_month_id 1.019 ***
## (0.049)
## as.factor(id)44:year_month_id 0.991 ***
## (0.073)
## as.factor(id)45:year_month_id 0.588 ***
## (0.093)
## as.factor(id)46:year_month_id 1.103 ***
## (0.001)
## as.factor(id)47:year_month_id 0.425 ***
## (0.080)
## -------------------------------------------
## R^2 0.899
## Adj. R^2 0.888
## Num. obs. 1551
## RMSE 3.972
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_OLS_trend")
# Event study graph
graph_yoy_hogo_households_OLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_OLS_trend")
graph_yoy_hogo_households_OLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_households_OLS_trend <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -0.823
## (3.680)
## treat_var:date_2018_03 1.291
## (4.791)
## treat_var:date_2018_04 -2.323
## (5.861)
## treat_var:date_2018_05 -2.441
## (5.365)
## treat_var:date_2018_06 -8.941
## (6.019)
## treat_var:date_2018_07 -6.355
## (6.622)
## treat_var:date_2018_08 3.260
## (8.651)
## treat_var:date_2018_09 -3.393
## (8.339)
## treat_var:date_2018_10 0.517
## (8.302)
## treat_var:date_2018_11 -3.124
## (8.775)
## treat_var:date_2018_12 -8.128
## (8.565)
## treat_var:date_2019_01 -12.504
## (8.961)
## treat_var:date_2019_02 -15.750 *
## (7.651)
## treat_var:date_2019_03 -19.812 *
## (7.453)
## treat_var:date_2019_04 -20.290 *
## (8.307)
## treat_var:date_2019_05 -18.190 *
## (8.574)
## treat_var:date_2019_06 -15.690 *
## (7.497)
## treat_var:date_2019_07 -16.286 *
## (6.780)
## treat_var:date_2019_08 -19.281 **
## (6.006)
## treat_var:date_2019_09 -15.173 **
## (5.493)
## treat_var:date_2019_10 -18.850 ***
## (5.307)
## treat_var:date_2019_11 -15.476 **
## (4.767)
## treat_var:date_2019_12 -4.525
## (3.634)
## treat_var:date_2020_02 10.211 **
## (3.772)
## treat_var:date_2020_03 18.598 **
## (6.038)
## treat_var:date_2020_04 34.379 ***
## (7.014)
## treat_var:date_2020_05 45.941 ***
## (9.194)
## treat_var:date_2020_06 64.657 ***
## (10.878)
## treat_var:date_2020_07 65.634 ***
## (11.214)
## treat_var:date_2020_08 69.110 ***
## (11.902)
## treat_var:date_2020_09 73.343 ***
## (12.891)
## as.factor(id)1:year_month_id -0.013 **
## (0.004)
## as.factor(id)2:year_month_id -0.228 ***
## (0.049)
## as.factor(id)3:year_month_id 0.164
## (0.104)
## as.factor(id)4:year_month_id -0.092
## (0.046)
## as.factor(id)5:year_month_id -0.212 ***
## (0.034)
## as.factor(id)6:year_month_id -0.191 ***
## (0.032)
## as.factor(id)7:year_month_id -0.330 ***
## (0.069)
## as.factor(id)8:year_month_id -0.738 ***
## (0.002)
## as.factor(id)9:year_month_id 0.156 ***
## (0.002)
## as.factor(id)10:year_month_id -0.311 ***
## (0.005)
## as.factor(id)11:year_month_id 0.035
## (0.018)
## as.factor(id)12:year_month_id -0.321 ***
## (0.015)
## as.factor(id)13:year_month_id -0.297 ***
## (0.001)
## as.factor(id)14:year_month_id 0.107 ***
## (0.007)
## as.factor(id)15:year_month_id -0.339 ***
## (0.019)
## as.factor(id)16:year_month_id 0.034 ***
## (0.006)
## as.factor(id)17:year_month_id 0.368 ***
## (0.025)
## as.factor(id)18:year_month_id 0.346 ***
## (0.032)
## as.factor(id)19:year_month_id -0.347 ***
## (0.018)
## as.factor(id)20:year_month_id 0.061
## (0.031)
## as.factor(id)21:year_month_id -0.317 ***
## (0.035)
## as.factor(id)22:year_month_id 0.043 *
## (0.020)
## as.factor(id)23:year_month_id 0.024
## (0.029)
## as.factor(id)24:year_month_id 0.250 ***
## (0.002)
## as.factor(id)25:year_month_id 0.018
## (0.009)
## as.factor(id)26:year_month_id -0.377 ***
## (0.021)
## as.factor(id)27:year_month_id -0.153 ***
## (0.011)
## as.factor(id)28:year_month_id -0.279 ***
## (0.005)
## as.factor(id)29:year_month_id -0.954 ***
## (0.001)
## as.factor(id)30:year_month_id -1.250 ***
## (0.018)
## as.factor(id)31:year_month_id -0.537 ***
## (0.033)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -0.053
## (0.043)
## as.factor(id)34:year_month_id 0.232 ***
## (0.008)
## as.factor(id)35:year_month_id -0.215 ***
## (0.032)
## as.factor(id)36:year_month_id 0.349 ***
## (0.026)
## as.factor(id)37:year_month_id -0.215 ***
## (0.034)
## as.factor(id)38:year_month_id -0.075 *
## (0.029)
## as.factor(id)39:year_month_id -0.225 **
## (0.072)
## as.factor(id)40:year_month_id 0.212 ***
## (0.005)
## as.factor(id)41:year_month_id 0.289 **
## (0.091)
## as.factor(id)42:year_month_id -0.176
## (0.089)
## as.factor(id)43:year_month_id 0.652 ***
## (0.026)
## as.factor(id)44:year_month_id 0.609 ***
## (0.006)
## as.factor(id)45:year_month_id 0.190 ***
## (0.012)
## as.factor(id)46:year_month_id 0.772 ***
## (0.070)
## as.factor(id)47:year_month_id 0.033 ***
## (0.000)
## -------------------------------------------
## R^2 0.940
## Adj. R^2 0.933
## Num. obs. 1551
## RMSE 162.660
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_WLS_trend")
# Event study graph
graph_yoy_hogo_households_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_WLS_trend")
graph_yoy_hogo_households_WLS_trend## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
estimates_yoy_hogo_households_WLS_trend <- df_estimates #for robustness check
results_yoy_hogo_households_WLS_trend <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_WLS_trend")
# Event study graph
graph_yoy_hogo_households_WLS_trend_onlypost <- event_study_graph(data = df_estimates ,
graph_title = "yoy_hogo_households_WLS_trend")
ggplotly(graph_yoy_hogo_households_WLS_trend_onlypost)estimates_yoy_hogo_households_WLS_trend_onlypost <- df_estimates #for robustness check
results_yoy_hogo_households_WLS_trend_onlypost <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ===================================================================
## Model 1
## -------------------------------------------------------------------
## treat_var:date_2018_01 15.161
## (16.381)
## treat_var:date_2018_02 12.677
## (16.343)
## treat_var:date_2018_03 15.377
## (15.871)
## treat_var:date_2018_04 11.404
## (15.676)
## treat_var:date_2018_05 8.129
## (15.670)
## treat_var:date_2018_06 4.589
## (15.322)
## treat_var:date_2018_07 4.466
## (15.678)
## treat_var:date_2018_08 19.620
## (23.377)
## treat_var:date_2018_09 8.684
## (15.543)
## treat_var:date_2018_10 12.498
## (14.793)
## treat_var:date_2018_11 8.115
## (14.076)
## treat_var:date_2018_12 0.855
## (13.363)
## treat_var:date_2019_01 -5.835
## (13.562)
## treat_var:date_2019_02 -9.032
## (10.817)
## treat_var:date_2019_03 -13.594
## (10.026)
## treat_var:date_2019_04 -14.865
## (11.496)
## treat_var:date_2019_05 -10.333
## (10.486)
## treat_var:date_2019_06 -9.963
## (9.307)
## treat_var:date_2019_07 -10.099
## (8.399)
## treat_var:date_2019_08 -12.271
## (7.181)
## treat_var:date_2019_09 -12.071
## (6.299)
## treat_var:date_2019_10 -16.015 *
## (6.094)
## treat_var:date_2019_11 -13.254 **
## (4.796)
## treat_var:date_2019_12 -3.804
## (3.749)
## treat_var:date_2020_02 11.916
## (9.833)
## treat_var:date_2020_03 18.884
## (10.893)
## treat_var:date_2020_04 25.472 *
## (10.640)
## treat_var:date_2020_05 25.797 **
## (9.548)
## treat_var:date_2020_06 27.668 **
## (9.705)
## treat_var:date_2020_07 25.679 *
## (10.713)
## treat_var:date_2020_08 23.696
## (12.339)
## treat_var:date_2020_09 24.784
## (16.278)
## date_2020_02:google_mobility_index_2020may 0.573
## (0.454)
## date_2020_03:google_mobility_index_2020may 0.626
## (0.442)
## date_2020_04:google_mobility_index_2020may 0.505
## (0.453)
## date_2020_05:google_mobility_index_2020may 0.233
## (0.520)
## date_2020_06:google_mobility_index_2020may -0.139
## (0.475)
## date_2020_07:google_mobility_index_2020may -0.141
## (0.527)
## date_2020_08:google_mobility_index_2020may -0.693
## (0.549)
## date_2020_09:google_mobility_index_2020may -0.754
## (0.651)
## date_2020_02:infection_rate_cumulative2020jun 0.094
## (0.349)
## date_2020_03:infection_rate_cumulative2020jun -0.116
## (0.376)
## date_2020_04:infection_rate_cumulative2020jun -0.144
## (0.384)
## date_2020_05:infection_rate_cumulative2020jun -0.447
## (0.480)
## date_2020_06:infection_rate_cumulative2020jun -0.430
## (0.475)
## date_2020_07:infection_rate_cumulative2020jun -0.507
## (0.534)
## date_2020_08:infection_rate_cumulative2020jun -0.847
## (0.592)
## date_2020_09:infection_rate_cumulative2020jun -0.876
## (0.616)
## date_2020_02:death_rate_cumulative2020jun 0.953
## (3.510)
## date_2020_03:death_rate_cumulative2020jun 2.131
## (3.913)
## date_2020_04:death_rate_cumulative2020jun 1.906
## (4.121)
## date_2020_05:death_rate_cumulative2020jun 4.601
## (4.946)
## date_2020_06:death_rate_cumulative2020jun 4.160
## (4.911)
## date_2020_07:death_rate_cumulative2020jun 5.696
## (5.228)
## date_2020_08:death_rate_cumulative2020jun 8.856
## (5.964)
## date_2020_09:death_rate_cumulative2020jun 8.939
## (6.166)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_02:Secondary_industry_ratio -2.922
## (36.183)
## date_2020_03:Secondary_industry_ratio -13.141
## (41.292)
## date_2020_04:Secondary_industry_ratio -4.419
## (39.101)
## date_2020_05:Secondary_industry_ratio 8.552
## (41.834)
## date_2020_06:Secondary_industry_ratio 32.745
## (42.441)
## date_2020_07:Secondary_industry_ratio 52.171
## (41.172)
## date_2020_08:Secondary_industry_ratio 60.082
## (42.292)
## date_2020_09:Secondary_industry_ratio 79.020
## (48.413)
## date_2020_02:Tertiary_industry_ratio 0.926
## (52.896)
## date_2020_03:Tertiary_industry_ratio 0.975
## (61.324)
## date_2020_04:Tertiary_industry_ratio 14.231
## (55.179)
## date_2020_05:Tertiary_industry_ratio 37.037
## (61.768)
## date_2020_06:Tertiary_industry_ratio 50.199
## (63.841)
## date_2020_07:Tertiary_industry_ratio 43.803
## (60.566)
## date_2020_08:Tertiary_industry_ratio 58.200
## (62.033)
## date_2020_09:Tertiary_industry_ratio 63.985
## (63.051)
## date_2020_02:Total_population -0.000
## (0.006)
## date_2020_03:Total_population 0.000
## (0.006)
## date_2020_04:Total_population 0.002
## (0.006)
## date_2020_05:Total_population -0.001
## (0.007)
## date_2020_06:Total_population 0.003
## (0.007)
## date_2020_07:Total_population 0.001
## (0.006)
## date_2020_08:Total_population 0.002
## (0.007)
## date_2020_09:Total_population 0.004
## (0.007)
## date_2020_02:Ratio_of_aged_population -0.330
## (0.167)
## date_2020_03:Ratio_of_aged_population -0.403 *
## (0.174)
## date_2020_04:Ratio_of_aged_population -0.420 *
## (0.202)
## date_2020_05:Ratio_of_aged_population -0.459 *
## (0.225)
## date_2020_06:Ratio_of_aged_population -0.271
## (0.215)
## date_2020_07:Ratio_of_aged_population -0.234
## (0.238)
## date_2020_08:Ratio_of_aged_population 0.042
## (0.267)
## date_2020_09:Ratio_of_aged_population 0.130
## (0.318)
## -------------------------------------------------------------------
## R^2 0.826
## Adj. R^2 0.804
## Num. obs. 1551
## RMSE 5.253
## N Clusters 47
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_OLS_notrend")
# Event study graph
graph_yoy_hogo_households_OLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_OLS_notrend")
estimates_yoy_hogo_households_OLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)##
## ===================================================================
## Model 1
## -------------------------------------------------------------------
## treat_var:date_2018_01 22.598
## (16.637)
## treat_var:date_2018_02 20.835
## (16.235)
## treat_var:date_2018_03 22.009
## (14.806)
## treat_var:date_2018_04 17.455
## (13.899)
## treat_var:date_2018_05 16.397
## (15.061)
## treat_var:date_2018_06 8.956
## (14.782)
## treat_var:date_2018_07 10.603
## (15.107)
## treat_var:date_2018_08 19.277
## (16.055)
## treat_var:date_2018_09 11.684
## (14.093)
## treat_var:date_2018_10 14.655
## (13.724)
## treat_var:date_2018_11 10.073
## (13.875)
## treat_var:date_2018_12 4.129
## (12.143)
## treat_var:date_2019_01 -1.181
## (12.368)
## treat_var:date_2019_02 -5.369
## (10.240)
## treat_var:date_2019_03 -10.373
## (9.683)
## treat_var:date_2019_04 -11.793
## (10.821)
## treat_var:date_2019_05 -10.636
## (10.370)
## treat_var:date_2019_06 -9.078
## (9.348)
## treat_var:date_2019_07 -10.616
## (8.343)
## treat_var:date_2019_08 -14.553
## (7.304)
## treat_var:date_2019_09 -11.387
## (6.757)
## treat_var:date_2019_10 -16.007 *
## (6.033)
## treat_var:date_2019_11 -13.574 **
## (4.766)
## treat_var:date_2019_12 -3.566
## (3.663)
## treat_var:date_2020_02 18.724
## (10.618)
## treat_var:date_2020_03 27.819 *
## (12.036)
## treat_var:date_2020_04 35.617 **
## (11.471)
## treat_var:date_2020_05 39.234 **
## (11.721)
## treat_var:date_2020_06 44.012 **
## (12.567)
## treat_var:date_2020_07 40.731 **
## (12.571)
## treat_var:date_2020_08 38.467 **
## (13.546)
## treat_var:date_2020_09 38.998 *
## (15.672)
## date_2020_02:google_mobility_index_2020may 0.813
## (0.465)
## date_2020_03:google_mobility_index_2020may 0.923
## (0.489)
## date_2020_04:google_mobility_index_2020may 0.945 *
## (0.466)
## date_2020_05:google_mobility_index_2020may 0.684
## (0.547)
## date_2020_06:google_mobility_index_2020may 0.444
## (0.578)
## date_2020_07:google_mobility_index_2020may 0.415
## (0.566)
## date_2020_08:google_mobility_index_2020may 0.031
## (0.577)
## date_2020_09:google_mobility_index_2020may -0.164
## (0.594)
## date_2020_02:infection_rate_cumulative2020jun 0.065
## (0.323)
## date_2020_03:infection_rate_cumulative2020jun -0.111
## (0.371)
## date_2020_04:infection_rate_cumulative2020jun -0.077
## (0.329)
## date_2020_05:infection_rate_cumulative2020jun -0.202
## (0.427)
## date_2020_06:infection_rate_cumulative2020jun -0.317
## (0.466)
## date_2020_07:infection_rate_cumulative2020jun -0.471
## (0.462)
## date_2020_08:infection_rate_cumulative2020jun -0.613
## (0.472)
## date_2020_09:infection_rate_cumulative2020jun -0.839
## (0.460)
## date_2020_02:death_rate_cumulative2020jun 0.545
## (3.494)
## date_2020_03:death_rate_cumulative2020jun 1.748
## (4.128)
## date_2020_04:death_rate_cumulative2020jun 1.302
## (3.790)
## date_2020_05:death_rate_cumulative2020jun 1.575
## (4.683)
## date_2020_06:death_rate_cumulative2020jun 2.506
## (5.186)
## date_2020_07:death_rate_cumulative2020jun 4.900
## (5.091)
## date_2020_08:death_rate_cumulative2020jun 5.944
## (5.271)
## date_2020_09:death_rate_cumulative2020jun 7.807
## (5.092)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_02:Secondary_industry_ratio 3.362
## (34.788)
## date_2020_03:Secondary_industry_ratio 3.014
## (38.962)
## date_2020_04:Secondary_industry_ratio 3.392
## (38.592)
## date_2020_05:Secondary_industry_ratio 17.260
## (45.650)
## date_2020_06:Secondary_industry_ratio 42.751
## (48.059)
## date_2020_07:Secondary_industry_ratio 60.959
## (46.721)
## date_2020_08:Secondary_industry_ratio 78.051
## (47.854)
## date_2020_09:Secondary_industry_ratio 93.264
## (51.540)
## date_2020_02:Tertiary_industry_ratio 22.979
## (49.972)
## date_2020_03:Tertiary_industry_ratio 40.153
## (57.909)
## date_2020_04:Tertiary_industry_ratio 44.579
## (53.117)
## date_2020_05:Tertiary_industry_ratio 70.248
## (63.896)
## date_2020_06:Tertiary_industry_ratio 90.371
## (68.651)
## date_2020_07:Tertiary_industry_ratio 83.605
## (65.171)
## date_2020_08:Tertiary_industry_ratio 103.134
## (65.553)
## date_2020_09:Tertiary_industry_ratio 116.745
## (64.600)
## date_2020_02:Total_population -0.002
## (0.004)
## date_2020_03:Total_population -0.003
## (0.005)
## date_2020_04:Total_population -0.001
## (0.005)
## date_2020_05:Total_population -0.003
## (0.005)
## date_2020_06:Total_population 0.001
## (0.006)
## date_2020_07:Total_population -0.000
## (0.006)
## date_2020_08:Total_population 0.002
## (0.006)
## date_2020_09:Total_population 0.005
## (0.006)
## date_2020_02:Ratio_of_aged_population -0.404 *
## (0.176)
## date_2020_03:Ratio_of_aged_population -0.419 *
## (0.192)
## date_2020_04:Ratio_of_aged_population -0.482 *
## (0.194)
## date_2020_05:Ratio_of_aged_population -0.491 *
## (0.229)
## date_2020_06:Ratio_of_aged_population -0.361
## (0.247)
## date_2020_07:Ratio_of_aged_population -0.325
## (0.244)
## date_2020_08:Ratio_of_aged_population -0.143
## (0.259)
## date_2020_09:Ratio_of_aged_population 0.022
## (0.290)
## -------------------------------------------------------------------
## R^2 0.903
## Adj. R^2 0.890
## Num. obs. 1551
## RMSE 208.817
## N Clusters 47
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_WLS_notrend")
# Event study graph
graph_yoy_hogo_households_WLS_notrend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_WLS_notrend")
estimates_yoy_hogo_households_WLS_notrend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -1.852
## (4.670)
## treat_var:date_2018_03 1.480
## (6.587)
## treat_var:date_2018_04 -1.862
## (6.972)
## treat_var:date_2018_05 -4.505
## (7.772)
## treat_var:date_2018_06 -7.413
## (7.636)
## treat_var:date_2018_07 -6.905
## (9.001)
## treat_var:date_2018_08 8.881
## (16.019)
## treat_var:date_2018_09 -1.423
## (11.147)
## treat_var:date_2018_10 3.022
## (11.161)
## treat_var:date_2018_11 -0.728
## (10.961)
## treat_var:date_2018_12 -7.357
## (10.649)
## treat_var:date_2019_01 -13.415
## (11.749)
## treat_var:date_2019_02 -15.980
## (9.490)
## treat_var:date_2019_03 -19.911 *
## (8.853)
## treat_var:date_2019_04 -20.551
## (10.858)
## treat_var:date_2019_05 -15.387
## (9.542)
## treat_var:date_2019_06 -14.385
## (8.688)
## treat_var:date_2019_07 -13.889
## (8.163)
## treat_var:date_2019_08 -15.429 *
## (6.817)
## treat_var:date_2019_09 -14.598 *
## (6.060)
## treat_var:date_2019_10 -17.910 **
## (5.800)
## treat_var:date_2019_11 -14.517 **
## (4.676)
## treat_var:date_2019_12 -4.436
## (3.835)
## treat_var:date_2020_02 5.359
## (12.125)
## treat_var:date_2020_03 12.405
## (12.503)
## treat_var:date_2020_04 19.072
## (15.723)
## treat_var:date_2020_05 19.476
## (17.819)
## treat_var:date_2020_06 21.426
## (21.124)
## treat_var:date_2020_07 19.515
## (26.896)
## treat_var:date_2020_08 17.611
## (30.409)
## treat_var:date_2020_09 18.777
## (37.464)
## date_2020_02:google_mobility_index_2020may 0.164
## (0.561)
## date_2020_03:google_mobility_index_2020may 0.185
## (0.607)
## date_2020_04:google_mobility_index_2020may 0.033
## (0.770)
## date_2020_05:google_mobility_index_2020may -0.271
## (0.758)
## date_2020_06:google_mobility_index_2020may -0.674
## (0.842)
## date_2020_07:google_mobility_index_2020may -0.708
## (0.925)
## date_2020_08:google_mobility_index_2020may -1.291
## (1.051)
## date_2020_09:google_mobility_index_2020may -1.384
## (1.220)
## date_2020_02:infection_rate_cumulative2020jun 0.210
## (0.272)
## date_2020_03:infection_rate_cumulative2020jun 0.010
## (0.317)
## date_2020_04:infection_rate_cumulative2020jun -0.010
## (0.379)
## date_2020_05:infection_rate_cumulative2020jun -0.303
## (0.474)
## date_2020_06:infection_rate_cumulative2020jun -0.277
## (0.476)
## date_2020_07:infection_rate_cumulative2020jun -0.346
## (0.540)
## date_2020_08:infection_rate_cumulative2020jun -0.677
## (0.611)
## date_2020_09:infection_rate_cumulative2020jun -0.696
## (0.633)
## date_2020_02:death_rate_cumulative2020jun -2.136
## (2.676)
## date_2020_03:death_rate_cumulative2020jun -1.195
## (3.329)
## date_2020_04:death_rate_cumulative2020jun -1.658
## (3.928)
## date_2020_05:death_rate_cumulative2020jun 0.800
## (4.718)
## date_2020_06:death_rate_cumulative2020jun 0.121
## (4.699)
## date_2020_07:death_rate_cumulative2020jun 1.420
## (5.332)
## date_2020_08:death_rate_cumulative2020jun 4.342
## (6.018)
## date_2020_09:death_rate_cumulative2020jun 4.188
## (6.312)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.003 *
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.003 *
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.003 *
## (0.002)
## date_2020_02:Secondary_industry_ratio 30.364
## (38.999)
## date_2020_03:Secondary_industry_ratio 22.705
## (43.385)
## date_2020_04:Secondary_industry_ratio 33.988
## (53.598)
## date_2020_05:Secondary_industry_ratio 49.520
## (53.301)
## date_2020_06:Secondary_industry_ratio 76.273
## (62.148)
## date_2020_07:Secondary_industry_ratio 98.259
## (69.836)
## date_2020_08:Secondary_industry_ratio 108.731
## (79.059)
## date_2020_09:Secondary_industry_ratio 130.229
## (94.368)
## date_2020_02:Tertiary_industry_ratio 36.803
## (36.631)
## date_2020_03:Tertiary_industry_ratio 39.612
## (42.576)
## date_2020_04:Tertiary_industry_ratio 55.628
## (51.887)
## date_2020_05:Tertiary_industry_ratio 81.194
## (54.792)
## date_2020_06:Tertiary_industry_ratio 97.116
## (60.393)
## date_2020_07:Tertiary_industry_ratio 93.479
## (67.071)
## date_2020_08:Tertiary_industry_ratio 110.636
## (75.261)
## date_2020_09:Tertiary_industry_ratio 119.181
## (84.332)
## date_2020_02:Total_population -0.003
## (0.004)
## date_2020_03:Total_population -0.003
## (0.006)
## date_2020_04:Total_population -0.002
## (0.006)
## date_2020_05:Total_population -0.005
## (0.007)
## date_2020_06:Total_population -0.001
## (0.008)
## date_2020_07:Total_population -0.004
## (0.009)
## date_2020_08:Total_population -0.003
## (0.010)
## date_2020_09:Total_population -0.000
## (0.012)
## date_2020_02:Ratio_of_aged_population -0.040
## (0.241)
## date_2020_03:Ratio_of_aged_population -0.091
## (0.276)
## date_2020_04:Ratio_of_aged_population -0.086
## (0.336)
## date_2020_05:Ratio_of_aged_population -0.103
## (0.332)
## date_2020_06:Ratio_of_aged_population 0.108
## (0.374)
## date_2020_07:Ratio_of_aged_population 0.167
## (0.396)
## date_2020_08:Ratio_of_aged_population 0.465
## (0.447)
## date_2020_09:Ratio_of_aged_population 0.576
## (0.522)
## as.factor(id)1:year_month_id
##
## as.factor(id)2:year_month_id -0.298
## (0.191)
## as.factor(id)3:year_month_id -0.177
## (0.163)
## as.factor(id)4:year_month_id -0.504 **
## (0.180)
## as.factor(id)5:year_month_id -0.381 *
## (0.167)
## as.factor(id)6:year_month_id -0.320 **
## (0.119)
## as.factor(id)7:year_month_id -0.633 ***
## (0.156)
## as.factor(id)8:year_month_id -0.911 ***
## (0.116)
## as.factor(id)9:year_month_id -0.021
## (0.129)
## as.factor(id)10:year_month_id -0.564 **
## (0.174)
## as.factor(id)11:year_month_id -0.312 *
## (0.125)
## as.factor(id)12:year_month_id -0.576 ***
## (0.141)
## as.factor(id)13:year_month_id -0.940 ***
## (0.155)
## as.factor(id)14:year_month_id -0.581 **
## (0.196)
## as.factor(id)15:year_month_id -0.568 ***
## (0.117)
## as.factor(id)16:year_month_id -0.155
## (0.155)
## as.factor(id)17:year_month_id 0.111
## (0.168)
## as.factor(id)18:year_month_id 0.097
## (0.123)
## as.factor(id)19:year_month_id -0.623 ***
## (0.172)
## as.factor(id)20:year_month_id -0.206
## (0.184)
## as.factor(id)21:year_month_id -0.490 **
## (0.144)
## as.factor(id)22:year_month_id -0.227
## (0.172)
## as.factor(id)23:year_month_id -0.218
## (0.142)
## as.factor(id)24:year_month_id -0.030
## (0.159)
## as.factor(id)25:year_month_id -0.192
## (0.146)
## as.factor(id)26:year_month_id -0.620 ***
## (0.168)
## as.factor(id)27:year_month_id -0.687 ***
## (0.142)
## as.factor(id)28:year_month_id -0.601 ***
## (0.131)
## as.factor(id)29:year_month_id -1.315 ***
## (0.160)
## as.factor(id)30:year_month_id -1.325 ***
## (0.157)
## as.factor(id)31:year_month_id -0.736 ***
## (0.150)
## as.factor(id)32:year_month_id -0.168
## (0.152)
## as.factor(id)33:year_month_id -0.391 *
## (0.148)
## as.factor(id)34:year_month_id -0.061
## (0.142)
## as.factor(id)35:year_month_id -0.603 **
## (0.189)
## as.factor(id)36:year_month_id 0.080
## (0.170)
## as.factor(id)37:year_month_id -0.603 **
## (0.180)
## as.factor(id)38:year_month_id -0.267 *
## (0.116)
## as.factor(id)39:year_month_id -0.487 **
## (0.139)
## as.factor(id)40:year_month_id -0.071
## (0.131)
## as.factor(id)41:year_month_id -0.070
## (0.159)
## as.factor(id)42:year_month_id -0.631 ***
## (0.173)
## as.factor(id)43:year_month_id 0.455 ***
## (0.121)
## as.factor(id)44:year_month_id 0.471 ***
## (0.123)
## as.factor(id)45:year_month_id 0.156
## (0.187)
## as.factor(id)46:year_month_id 0.435 **
## (0.135)
## as.factor(id)47:year_month_id -0.187
## (0.266)
## --------------------------------------------------------------------
## R^2 0.912
## Adj. R^2 0.897
## Num. obs. 1551
## RMSE 3.797
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_OLS_trend")
# Event study graph
graph_yoy_hogo_households_OLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_OLS_trend")
estimates_yoy_hogo_households_OLS_trend_covar <- df_estimates #for robustness check# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -0.823
## (3.768)
## treat_var:date_2018_03 1.292
## (4.906)
## treat_var:date_2018_04 -2.322
## (6.002)
## treat_var:date_2018_05 -2.440
## (5.493)
## treat_var:date_2018_06 -8.941
## (6.161)
## treat_var:date_2018_07 -6.353
## (6.776)
## treat_var:date_2018_08 3.261
## (8.855)
## treat_var:date_2018_09 -3.391
## (8.537)
## treat_var:date_2018_10 0.519
## (8.499)
## treat_var:date_2018_11 -3.122
## (8.980)
## treat_var:date_2018_12 -8.126
## (8.771)
## treat_var:date_2019_01 -12.502
## (9.175)
## treat_var:date_2019_02 -15.748
## (7.834)
## treat_var:date_2019_03 -19.810 *
## (7.633)
## treat_var:date_2019_04 -20.288 *
## (8.505)
## treat_var:date_2019_05 -18.188 *
## (8.780)
## treat_var:date_2019_06 -15.688 *
## (7.676)
## treat_var:date_2019_07 -16.284 *
## (6.944)
## treat_var:date_2019_08 -19.279 **
## (6.152)
## treat_var:date_2019_09 -15.171 **
## (5.624)
## treat_var:date_2019_10 -18.848 **
## (5.437)
## treat_var:date_2019_11 -15.474 **
## (4.894)
## treat_var:date_2019_12 -4.523
## (3.727)
## treat_var:date_2020_02 8.882
## (10.764)
## treat_var:date_2020_03 18.090
## (11.845)
## treat_var:date_2020_04 26.000
## (13.746)
## treat_var:date_2020_05 29.730
## (15.103)
## treat_var:date_2020_06 34.621
## (18.341)
## treat_var:date_2020_07 31.453
## (21.859)
## treat_var:date_2020_08 29.302
## (24.263)
## treat_var:date_2020_09 29.946
## (28.789)
## date_2020_02:google_mobility_index_2020may 0.525
## (0.451)
## date_2020_03:google_mobility_index_2020may 0.613
## (0.515)
## date_2020_04:google_mobility_index_2020may 0.613
## (0.584)
## date_2020_05:google_mobility_index_2020may 0.330
## (0.593)
## date_2020_06:google_mobility_index_2020may 0.068
## (0.692)
## date_2020_07:google_mobility_index_2020may 0.016
## (0.736)
## date_2020_08:google_mobility_index_2020may -0.390
## (0.815)
## date_2020_09:google_mobility_index_2020may -0.607
## (0.932)
## date_2020_02:infection_rate_cumulative2020jun 0.266
## (0.245)
## date_2020_03:infection_rate_cumulative2020jun 0.106
## (0.298)
## date_2020_04:infection_rate_cumulative2020jun 0.155
## (0.267)
## date_2020_05:infection_rate_cumulative2020jun 0.046
## (0.366)
## date_2020_06:infection_rate_cumulative2020jun -0.054
## (0.400)
## date_2020_07:infection_rate_cumulative2020jun -0.192
## (0.393)
## date_2020_08:infection_rate_cumulative2020jun -0.319
## (0.413)
## date_2020_09:infection_rate_cumulative2020jun -0.529
## (0.388)
## date_2020_02:death_rate_cumulative2020jun -2.582
## (2.645)
## date_2020_03:death_rate_cumulative2020jun -1.619
## (3.394)
## date_2020_04:death_rate_cumulative2020jun -2.305
## (3.116)
## date_2020_05:death_rate_cumulative2020jun -2.272
## (4.000)
## date_2020_06:death_rate_cumulative2020jun -1.581
## (4.379)
## date_2020_07:death_rate_cumulative2020jun 0.573
## (4.340)
## date_2020_08:death_rate_cumulative2020jun 1.376
## (4.462)
## date_2020_09:death_rate_cumulative2020jun 2.999
## (4.250)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.002 *
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.003 **
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.003 *
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.003 *
## (0.001)
## date_2020_02:Secondary_industry_ratio 48.422
## (34.135)
## date_2020_03:Secondary_industry_ratio 51.533
## (40.148)
## date_2020_04:Secondary_industry_ratio 55.368
## (44.508)
## date_2020_05:Secondary_industry_ratio 72.695
## (45.279)
## date_2020_06:Secondary_industry_ratio 101.644
## (53.782)
## date_2020_07:Secondary_industry_ratio 123.310 *
## (58.007)
## date_2020_08:Secondary_industry_ratio 143.860 *
## (64.609)
## date_2020_09:Secondary_industry_ratio 162.530 *
## (75.168)
## date_2020_02:Tertiary_industry_ratio 70.389 *
## (33.514)
## date_2020_03:Tertiary_industry_ratio 91.198 *
## (42.568)
## date_2020_04:Tertiary_industry_ratio 99.259 *
## (41.016)
## date_2020_05:Tertiary_industry_ratio 128.564 **
## (47.669)
## date_2020_06:Tertiary_industry_ratio 152.323 **
## (53.957)
## date_2020_07:Tertiary_industry_ratio 149.192 **
## (54.166)
## date_2020_08:Tertiary_industry_ratio 172.358 **
## (58.537)
## date_2020_09:Tertiary_industry_ratio 189.604 **
## (60.418)
## date_2020_02:Total_population -0.003
## (0.003)
## date_2020_03:Total_population -0.003
## (0.004)
## date_2020_04:Total_population -0.002
## (0.005)
## date_2020_05:Total_population -0.004
## (0.004)
## date_2020_06:Total_population -0.000
## (0.006)
## date_2020_07:Total_population -0.001
## (0.007)
## date_2020_08:Total_population 0.000
## (0.007)
## date_2020_09:Total_population 0.004
## (0.008)
## date_2020_02:Ratio_of_aged_population -0.115
## (0.191)
## date_2020_03:Ratio_of_aged_population -0.109
## (0.224)
## date_2020_04:Ratio_of_aged_population -0.149
## (0.246)
## date_2020_05:Ratio_of_aged_population -0.136
## (0.250)
## date_2020_06:Ratio_of_aged_population 0.017
## (0.295)
## date_2020_07:Ratio_of_aged_population 0.075
## (0.301)
## date_2020_08:Ratio_of_aged_population 0.279
## (0.331)
## date_2020_09:Ratio_of_aged_population 0.466
## (0.393)
## as.factor(id)1:year_month_id 0.735 ***
## (0.118)
## as.factor(id)2:year_month_id 0.421 ***
## (0.064)
## as.factor(id)3:year_month_id 0.616 ***
## (0.125)
## as.factor(id)4:year_month_id 0.248
## (0.175)
## as.factor(id)5:year_month_id 0.316 **
## (0.103)
## as.factor(id)6:year_month_id 0.446 ***
## (0.100)
## as.factor(id)7:year_month_id 0.206
## (0.168)
## as.factor(id)8:year_month_id -0.090
## (0.124)
## as.factor(id)9:year_month_id 0.803 ***
## (0.125)
## as.factor(id)10:year_month_id 0.276
## (0.164)
## as.factor(id)11:year_month_id 0.474 **
## (0.155)
## as.factor(id)12:year_month_id 0.181
## (0.177)
## as.factor(id)13:year_month_id -0.066
## (0.157)
## as.factor(id)14:year_month_id 0.224
## (0.154)
## as.factor(id)15:year_month_id 0.156
## (0.127)
## as.factor(id)16:year_month_id 0.591 **
## (0.196)
## as.factor(id)17:year_month_id 0.923 ***
## (0.210)
## as.factor(id)18:year_month_id 0.781 ***
## (0.157)
## as.factor(id)19:year_month_id 0.226
## (0.174)
## as.factor(id)20:year_month_id 0.702 ***
## (0.186)
## as.factor(id)21:year_month_id 0.269
## (0.148)
## as.factor(id)22:year_month_id 0.586 **
## (0.167)
## as.factor(id)23:year_month_id 0.555 ***
## (0.140)
## as.factor(id)24:year_month_id 0.775 ***
## (0.147)
## as.factor(id)25:year_month_id 0.569 ***
## (0.142)
## as.factor(id)26:year_month_id 0.225
## (0.137)
## as.factor(id)27:year_month_id 0.096
## (0.105)
## as.factor(id)28:year_month_id 0.124
## (0.161)
## as.factor(id)29:year_month_id -0.638 ***
## (0.127)
## as.factor(id)30:year_month_id -0.600 ***
## (0.069)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 0.553 ***
## (0.092)
## as.factor(id)33:year_month_id 0.423 **
## (0.126)
## as.factor(id)34:year_month_id 0.678 ***
## (0.144)
## as.factor(id)35:year_month_id 0.135
## (0.166)
## as.factor(id)36:year_month_id 0.897 ***
## (0.102)
## as.factor(id)37:year_month_id 0.201
## (0.150)
## as.factor(id)38:year_month_id 0.519 ***
## (0.068)
## as.factor(id)39:year_month_id 0.336 **
## (0.109)
## as.factor(id)40:year_month_id 0.643 ***
## (0.168)
## as.factor(id)41:year_month_id 0.753 ***
## (0.149)
## as.factor(id)42:year_month_id 0.154
## (0.131)
## as.factor(id)43:year_month_id 1.221 ***
## (0.045)
## as.factor(id)44:year_month_id 1.200 ***
## (0.061)
## as.factor(id)45:year_month_id 0.866 ***
## (0.072)
## as.factor(id)46:year_month_id 1.190 ***
## (0.089)
## as.factor(id)47:year_month_id 0.671 ***
## (0.183)
## --------------------------------------------------------------------
## R^2 0.958
## Adj. R^2 0.951
## Num. obs. 1551
## RMSE 139.975
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_WLS_trend")
# Event study graph
graph_yoy_hogo_households_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_WLS_trend")
estimates_yoy_hogo_households_WLS_trend_covar <- df_estimates #for robustness check
results_yoy_hogo_households_WLS_trend_covar <- estimation_results # for only-post DID table# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$job_seeker_total_shock)
#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "job_seeker_total_shock",
estimation_label = "yoy_hogo_households_WLS_trend")
# Event study graph
graph_yoy_hogo_households_WLS_trend_covar_onlypost <- event_study_graph(data = df_estimates ,
graph_title = "yoy_hogo_households_WLS_trend")
ggplotly(graph_yoy_hogo_households_WLS_trend_covar_onlypost)estimates_yoy_hogo_households_WLS_trend_covar_onlypost <- df_estimates #for robustness check
results_yoy_hogo_households_WLS_trend_covar_onlypost <- estimation_results # for only-post DID table#merge and label estimates data
estimates_hogo_persons_bind <- dplyr::bind_rows(estimates_hogo_persons_OLS_notrend,
estimates_hogo_persons_WLS_notrend,
estimates_hogo_persons_OLS_trend,
estimates_hogo_persons_WLS_trend)
#change labels and reorder labels
estimates_hogo_persons_bind <- estimates_labeling_poverty(estimates_hogo_persons_bind)
#graph
graph_hogo_persons_bind <- event_study_graph_bind_main(data = estimates_hogo_persons_bind,
graph_title = "Public Assistance recipients")
ggplotly(graph_hogo_persons_bind)#merge and label estimates data
estimates_hogo_persons_bind_covar <- dplyr::bind_rows(estimates_hogo_persons_OLS_notrend_covar,
estimates_hogo_persons_WLS_notrend_covar,
estimates_hogo_persons_OLS_trend_covar,
estimates_hogo_persons_WLS_trend_covar)
#change labels and reorder labels
estimates_hogo_persons_bind_covar <- estimates_labeling_poverty(estimates_hogo_persons_bind_covar)
#graph
graph_hogo_persons_bind_covar <- event_study_graph_bind_main(data = estimates_hogo_persons_bind_covar,
graph_title = "Public Assistance recipients, with covariates")
ggplotly(graph_hogo_persons_bind_covar)#merge and label estimates data
estimates_yoy_hogo_persons_bind <- dplyr::bind_rows(estimates_yoy_hogo_persons_OLS_notrend,
estimates_yoy_hogo_persons_WLS_notrend,
estimates_yoy_hogo_persons_OLS_trend,
estimates_yoy_hogo_persons_WLS_trend)
#change labels and reorder labels
estimates_yoy_hogo_persons_bind <- estimates_labeling_poverty(estimates_yoy_hogo_persons_bind)
#graph
graph_yoy_hogo_persons_bind <- event_study_graph_bind_main(data = estimates_yoy_hogo_persons_bind,
graph_title = "Public Assistance recipients (year-on-year)")
ggplotly(graph_yoy_hogo_persons_bind)#merge and label estimates data
estimates_yoy_hogo_persons_bind_covar <- dplyr::bind_rows(estimates_yoy_hogo_persons_OLS_notrend_covar,
estimates_yoy_hogo_persons_WLS_notrend_covar,
estimates_yoy_hogo_persons_OLS_trend_covar,
estimates_yoy_hogo_persons_WLS_trend_covar)
#change labels and reorder labels
estimates_yoy_hogo_persons_bind_covar <- estimates_labeling_poverty(estimates_yoy_hogo_persons_bind_covar)
#graph
graph_yoy_hogo_persons_bind_covar <- event_study_graph_bind_main(data = estimates_yoy_hogo_persons_bind_covar,
graph_title = "Public Assistance recipients (year-on-year), with covariates")
ggplotly(graph_yoy_hogo_persons_bind_covar)#merge and label estimates data
estimates_hogo_households_bind <- dplyr::bind_rows(estimates_hogo_households_OLS_notrend,
estimates_hogo_households_WLS_notrend,
estimates_hogo_households_OLS_trend,
estimates_hogo_households_WLS_trend)
#change labels and reorder labels
estimates_hogo_households_bind <- estimates_labeling_poverty(estimates_hogo_households_bind)
#graph
graph_hogo_households_bind <- event_study_graph_bind_main(data = estimates_hogo_households_bind,
graph_title = "Public Assistance recipient households")
ggplotly(graph_hogo_households_bind)#merge and label estimates data
estimates_hogo_households_bind_covar <- dplyr::bind_rows(estimates_hogo_households_OLS_notrend_covar,
estimates_hogo_households_WLS_notrend_covar,
estimates_hogo_households_OLS_trend_covar,
estimates_hogo_households_WLS_trend_covar)
#change labels and reorder labels
estimates_hogo_households_bind_covar <- estimates_labeling_poverty(estimates_hogo_households_bind_covar)
#graph
graph_hogo_households_bind_covar <- event_study_graph_bind_main(data = estimates_hogo_households_bind_covar,
graph_title = "Public Assistance recipient households, with covariates")
ggplotly(graph_hogo_households_bind_covar)#merge and label estimates data
estimates_yoy_hogo_households_bind <- dplyr::bind_rows(estimates_yoy_hogo_households_OLS_notrend,
estimates_yoy_hogo_households_WLS_notrend,
estimates_yoy_hogo_households_OLS_trend,
estimates_yoy_hogo_households_WLS_trend)
#change labels and reorder labels
estimates_yoy_hogo_households_bind <- estimates_labeling_poverty(estimates_yoy_hogo_households_bind)
#graph
graph_yoy_hogo_households_bind <- event_study_graph_bind_main(data = estimates_yoy_hogo_households_bind,
graph_title = "Public Assistance recipient households (year-on-year)")
ggplotly(graph_yoy_hogo_households_bind)#merge and label estimates data
estimates_yoy_hogo_households_bind_covar <- dplyr::bind_rows(estimates_yoy_hogo_households_OLS_notrend_covar,
estimates_yoy_hogo_households_WLS_notrend_covar,
estimates_yoy_hogo_households_OLS_trend_covar,
estimates_yoy_hogo_households_WLS_trend_covar)
#change labels and reorder labels
estimates_yoy_hogo_households_bind_covar <- estimates_labeling_poverty(estimates_yoy_hogo_households_bind_covar)
#graph
graph_yoy_hogo_households_bind_covar <- event_study_graph_bind_main(data = estimates_yoy_hogo_households_bind_covar,
graph_title = "Public Assistance recipient households (year-on-year), with covariates")
ggplotly(graph_yoy_hogo_households_bind_covar)ggplotly(graph_hogo_persons_bind)ggplotly(graph_hogo_persons_bind_covar)ggplotly(graph_yoy_hogo_persons_bind)ggplotly(graph_yoy_hogo_persons_bind_covar)ggplotly(graph_hogo_households_bind)ggplotly(graph_hogo_households_bind_covar)ggplotly(graph_yoy_hogo_households_bind)ggplotly(graph_yoy_hogo_households_bind_covar)#Legendの表示
graph_for_legend <- graph_hogo_persons_bind +
theme(legend.position = 'bottom', # Adjust x axis label
legend.title = element_text(color = "black", size = 20),
legend.text = element_text(color = "black", size = 20))
graph_for_legend #extract legend
legend_model_types <- ggpubr::get_legend(graph_for_legend)
legend_model_types <- ggpubr::as_ggplot(legend_model_types)
legend_model_typesグラフを統合して論文用に保存。 ### graph size
dpi_num <- 100
width_num <- 15
height_num <- 10ymin <- - 50
ymax <- 150
ymin_num <- - 50
ymax_num <- 150
interval <- 50
graph_hogo_persons_WLS_trend <- graph_hogo_persons_WLS_trend +
labs(title = "(a) Public Assistance recipients") +
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_persons_WLS_trend_covar <- graph_hogo_persons_WLS_trend_covar +
labs(title = "(b) Public Assistance recipients, with covaraites") +
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_households_WLS_trend <- graph_hogo_households_WLS_trend +
labs(title = "(c) Public Assistance recipient households")+
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_households_WLS_trend_covar <- graph_hogo_households_WLS_trend_covar +
labs(title = "(d) Public Assistance recipient households, with covaraites") +
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph <- (graph_hogo_persons_WLS_trend | graph_hogo_persons_WLS_trend_covar) /
(graph_hogo_households_WLS_trend | graph_hogo_households_WLS_trend_covar)
graph## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
#保存
ggsave(file = "output/graph_job_seeker_total_shock_on_PAbenefit_WLStrends.pdf", plot = graph,
dpi = dpi_num, width = width_num, height = height_num) ## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
graph_hogo_persons_WLS_trend <- graph_yoy_hogo_persons_WLS_trend +
labs(title = "(a) Recipients") +
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_persons_WLS_trend_covar <- graph_yoy_hogo_persons_WLS_trend_covar +
labs(title = "(b) Recipients, with covaraites") +
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_households_WLS_trend <- graph_yoy_hogo_households_WLS_trend +
labs(title = "(c) Recipient households")+
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_households_WLS_trend_covar <- graph_yoy_hogo_households_WLS_trend_covar +
labs(title = "(d) Recipient households, with covaraites") +
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph <- (graph_hogo_persons_WLS_trend | graph_hogo_persons_WLS_trend_covar) /
(graph_hogo_households_WLS_trend | graph_hogo_households_WLS_trend_covar)
graph## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
#保存
ggsave(file = "output/graph_job_seeker_total_shock_on_yoy_PAbenefit_WLStrends.pdf", plot = graph,
dpi = dpi_num, width = width_num, height = height_num) ## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 row(s) containing missing values (geom_path).
ymin <- - 50
ymax <- 200
ymin_num <- - 50
ymax_num <- 200
interval <- 50
graph_hogo_persons_bind <- graph_hogo_persons_bind + labs(title = "(a) Public Assistance recipients")+ scale_y_continuous(limit = c(ymin,400), breaks=seq(ymin_num, 400, interval))
graph_hogo_persons_bind_covar <- graph_hogo_persons_bind_covar + labs(title = "(b) Public Assistance recipients with covariates")+ scale_y_continuous(limit = c(ymin, 400), breaks=seq(ymin_num, 400, interval))
graph_hogo_households_bind <- graph_hogo_households_bind + labs(title = "(c) Public Assistance recipient households")+ scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_hogo_households_bind_covar <- graph_hogo_households_bind_covar + labs(title = "(d) Public Assistance recipient households with covariates")+ scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph <- (graph_hogo_persons_bind + graph_hogo_persons_bind_covar) /
(graph_hogo_households_bind + graph_hogo_households_bind_covar) /
legend_model_types +
plot_layout(heights = c(2, 2, 0.5)) #0.3から0.5へ変更 2021Sep7 Waki
graph#保存
ggsave(file = "output/graph_job_seeker_total_shock_on_PAbenefit_robust.pdf", plot = graph,
dpi = dpi_num, width = width_num, height = height_num) ymin <- - 50
ymax <- 150
ymin_num <- - 50
ymax_num <- 150
interval <- 50
graph_yoy_hogo_persons_bind <- graph_yoy_hogo_persons_bind + labs(title = "(a) Recipients")+
scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_yoy_hogo_persons_bind_covar <- graph_yoy_hogo_persons_bind_covar + labs(title = "(b) Recipients, with covariates")+ scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_yoy_hogo_households_bind <- graph_yoy_hogo_households_bind + labs(title = "(c) Recipient households ")+ scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph_yoy_hogo_households_bind_covar <- graph_yoy_hogo_households_bind_covar + labs(title = "(d) Recipient households, with covariates")+ scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))
graph <- (graph_yoy_hogo_persons_bind + graph_yoy_hogo_persons_bind_covar) /
(graph_yoy_hogo_households_bind + graph_yoy_hogo_households_bind_covar) /
legend_model_types +
plot_layout(heights = c(2, 2, 0.5)) #0.3から0.5へ変更 2021Sep7 Waki
graph#保存
ggsave(file = "output/graph_job_seeker_total_shock_on_yoy_PAbenefit_robust.pdf", plot = graph,
dpi = dpi_num, width = width_num, height = height_num)
#ggplotlyoptions("modelsummary_format_numeric_latex" = "plain")
# 列の選択 column order
# 生活保護受給者、生活保護受給世帯、YOYのみ, monthlyhのみ
rows_MONTH <- tribble(~name, ~"(1)", ~"(2)", ~"(3)", ~"(4)",
"Ref. month", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}")
## results list
table_results_MONTH <- list()
table_results_MONTH[["(1)"]] <- results_yoy_hogo_persons_WLS_trend
table_results_MONTH[["(2)"]] <- results_yoy_hogo_persons_WLS_trend_onlypost
table_results_MONTH[["(3)"]] <- results_yoy_hogo_households_WLS_trend
table_results_MONTH[["(4)"]] <- results_yoy_hogo_households_WLS_trend_onlypost
## HTML table
estimates_table_MONTH(df = table_results_MONTH,
rows = rows_MONTH,
title_words = "Hogo",
gof = gm,
output_style = "html") %>%
kableExtra::add_header_above(c(" " = 1, "Recipients" = 2, "Recipient Households" = 2))
## Latex table
estimates_table_MONTH(df = table_results_MONTH,
rows = rows_MONTH,
gof = gm,
title_words = "DID estimates for suicide rates, with covariates\\label{tab:DID_unemploy_on_suicide_covar}",
output_style = "latex") %>%
kableExtra::add_header_above(c(" " = 1, "Recipients" = 2, "Recipient Households" = 2)) %>%
kableExtra::add_footnote(c("Notes: Robust standard errors are clustered at the prefecture level and the number of clusters (i.e. prefectures) is 47. The treatment variable is the COVID-19-induced employment shock, which is calculated as equation \\eqref{eq:employment_shock}. Estimates are obtained based on equation \\eqref{eq:did_model_ver2} with WLS estimation weighted by prefecture population size."),threeparttable = TRUE, notation = "none",escape = FALSE) %>%
kableExtra::column_spec(2:7, width = "1.5cm") %>%
kableExtra::save_kable("output/job_seeker_total_shock_on_PAbenefit_robust_tables.tex")# 列の選択 column order
# 生活保護受給者、生活保護受給世帯、YOYのみ, monthlyhのみ
rows_MONTH <- tribble(~name, ~"(1)", ~"(2)", ~"(3)", ~"(4)",
"Ref. month", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}")
## results list
table_results_MONTH <- list()
table_results_MONTH[["(1)"]] <- results_yoy_hogo_persons_WLS_trend_covar
table_results_MONTH[["(2)"]] <- results_yoy_hogo_persons_WLS_trend_covar_onlypost
table_results_MONTH[["(3)"]] <- results_yoy_hogo_households_WLS_trend_covar
table_results_MONTH[["(4)"]] <- results_yoy_hogo_households_WLS_trend_covar_onlypost
## HTML table
estimates_table_MONTH(df = table_results_MONTH,
rows = rows_MONTH,
title_words = "Hogo",
gof = gm,
output_style = "html") %>%
kableExtra::add_header_above(c(" " = 1, "Recipients" = 2, "Recipient Households" = 2))
## Latex table
estimates_table_MONTH(df = table_results_MONTH,
rows = rows_MONTH,
gof = gm,
title_words = "DID estimates for suicide rates, with covariates\\label{tab:DID_unemploy_on_suicide_covar}",
output_style = "latex") %>%
kableExtra::add_header_above(c(" " = 1, "Recipients" = 2, "Recipient Households" = 2)) %>%
kableExtra::add_footnote(c("Notes: Robust standard errors are clustered at the prefecture level and the number of clusters (i.e. prefectures) is 47. The treatment variable is the COVID-19-induced employment shock, which is calculated as equation \\eqref{eq:employment_shock}. Estimates are obtained based on equation \\eqref{eq:did_model_ver2} with WLS estimation weighted by prefecture population size."),threeparttable = TRUE, notation = "none",escape = FALSE) %>%
kableExtra::column_spec(2:7, width = "1.5cm") %>%
kableExtra::save_kable("output/job_seeker_total_shock_on_PAbenefit_robust_covar_tables.tex")